Introduction and Context

Train a CycleGAN model to translate images between the two domains, monet and real, demonstrating the power of CycleGAN in unpaired image-to-image translation

# This Colab notebook demonstrates implementing CycleGAN using the "pytorch-CycleGAN-and-pix2pix" framework.
# We'll be working with the "monet2photo" dataset.
# Our goal is to train a CycleGAN model to translate images between the two domains, monet and normal photo, demonstrating the power of CycleGAN in unpaired image-to-image translation.

# Framework link for reference:
framework_link = "https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
print(f"For more information about the CycleGAN and pix2pix implementation, visit: {framework_link}")

For more information about the CycleGAN and pix2pix implementation, visit: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

Check for GPU avilability

import tensorflow as tf

# Check GPU details
device_name = tf.test.gpu_device_name()
if device_name:
    print(f"GPU detected: {device_name}")
else:
    print("No GPU found. Please enable GPU runtime in Colab settings.")

!nvidia-smi

GPU detected: /device:GPU:0
Tue Nov 19 20:30:07 2024       
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| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |
| N/A   34C    P0              16W /  70W |    103MiB / 15360MiB |      2%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+

Installing the CycleGAN and Pix2Pix Framework

# Install the PyTorch CycleGAN and Pix2Pix framework from GitHub.
# This setup will allow us to utilize predefined models and data loaders specific to CycleGAN.
!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git
%cd pytorch-CycleGAN-and-pix2pix
!pip install -r requirements.txt



# The command above clones the GitHub repository for CycleGAN and installs the necessary dependencies.
# Ensure that the installation completes successfully before moving on to download the dataset.

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/content/pytorch-CycleGAN-and-pix2pix
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Downloading the Cityscapes Dataset

# Download the "monet2photo" dataset using the provided script from the CycleGAN framework.
# This dataset includes unpaired images of monet and real pictures, ideal for training CycleGAN.

# Run the command below to download and set up the monet2photo dataset in the appropriate directory.
!bash ./datasets/download_cyclegan_dataset.sh monet2photo

# After downloading, the dataset will be organized in the following structure:
# pytorch-CycleGAN-and-pix2pix/
# ├── datasets/
# │   └── monet2photo/
# │       ├── trainA/   # Contains monet images for training (Domain A)
# │       ├── trainB/   # Contains real images for training (Domain B)
# │       ├── testA/    # Contains monet images for testing (Domain A)
# │       └── testB/    # Contains real images for testing (Domain B)

# In this structure:
# - 'trainA' and 'testA' hold images from the monet domain.
# - 'trainB' and 'testB' hold images from the real domain.
# CycleGAN will use these unpaired images to learn a bidirectional mapping between monet and real pictures.

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Visualizing Unpaired Data Examples

trainA_files

trainB_files

import os
import matplotlib.pyplot as plt
from PIL import Image
import random

# Define paths to the training data for monet (trainA) and real (trainB) images
trainA_path = './datasets/monet2photo/trainA'  # monet images
trainB_path = './datasets/monet2photo/trainB'  # real images

# Function to load and display random examples of unpaired data
def visualize_unpaired_data(num_examples=3):
    # Get random file names from trainA and trainB directories
    trainA_files = random.sample(os.listdir(trainA_path), num_examples)
    trainB_files = random.sample(os.listdir(trainB_path), num_examples)

    # Set up a subplot grid for visualization
    fig, axes = plt.subplots(num_examples, 2, figsize=(10, 5 * num_examples))
    fig.suptitle("Unpaired Examples: monet Images (left) and real Images (right)", fontsize=16)

    for i in range(num_examples):
        # Load and display a random monet image from trainA
        imgA = Image.open(os.path.join(trainA_path, trainA_files[i]))
        axes[i, 0].imshow(imgA)
        axes[i, 0].axis('off')
        axes[i, 0].set_title("monet Image")

        # Load and display a random normal image from trainB
        imgB = Image.open(os.path.join(trainB_path, trainB_files[i]))
        axes[i, 1].imshow(imgB)
        axes[i, 1].axis('off')
        axes[i, 1].set_title("real Image")

    plt.tight_layout()
    plt.subplots_adjust(top=0.92)
    plt.show()

# Call the function to visualize 2 unpaired examples from the dataset
visualize_unpaired_data(num_examples=2)

# This cell visualizes unpaired data samples to help you understand the type of images in each domain.
# Each row shows a monet image on the left (from Domain A) and an unrelated real image on the right (from Domain B).


# CycleGAN will use these unpaired examples to learn the translation between monet and real pictures.

Basic Analytics on the Unpaired Dataset

Define paths to the training data for monet (trainA) and real (trainB) images

trainA_path = './datasets/monet2photo/trainA' # monet images

trainB_path = './datasets/monet2photo/trainB' # real images

import numpy as np

# Function to calculate and display basic analytics on the unpaired dataset
def dataset_analytics(data_path_A, data_path_B):
    # Count the number of images in each domain (monet and real)
    num_images_A = len(os.listdir(data_path_A))
    num_images_B = len(os.listdir(data_path_B))

    # Lists to store dimensions of images in each domain
    dimensions_A = []
    dimensions_B = []

    # Get dimensions for a sample of images in Domain A (monet) and Domain B (real)
    for img_file in os.listdir(data_path_A):
        img = Image.open(os.path.join(data_path_A, img_file))
        dimensions_A.append(img.size)

    for img_file in os.listdir(data_path_B):
        img = Image.open(os.path.join(data_path_B, img_file))
        dimensions_B.append(img.size)

    # Convert dimensions to numpy arrays for calculations
    dimensions_A = np.array(dimensions_A)
    dimensions_B = np.array(dimensions_B)

    # Calculate average dimensions
    avg_width_A, avg_height_A = dimensions_A.mean(axis=0)
    avg_width_B, avg_height_B = dimensions_B.mean(axis=0)

    # Display analytics
    print(f"Dataset Analytics for monet2photo")
    print(f"------------------------------------------")
    print(f"Total images in monet (trainA): {num_images_A}")
    print(f"Total images in real (trainB): {num_images_B}")

    print(f"Average dimensions for monet images: {avg_width_A:.2f} x {avg_height_A:.2f}")
    print(f"Average dimensions for real images: {avg_width_B:.2f} x {avg_height_B:.2f}")

    print(f"Image dimensions range for monet images: {dimensions_A.min(axis=0)} to {dimensions_A.max(axis=0)}")
    print(f"Image dimensions range for real images: {dimensions_B.min(axis=0)} to {dimensions_B.max(axis=0)}")

# Run analytics on the dataset
dataset_analytics(trainA_path, trainB_path)

# This cell provides basic analytics for the monet2photo dataset.
# It calculates the total number of images in each domain (monet and real) and finds the average image dimensions.
# These insights help to understand the dataset's size and characteristics before training the CycleGAN model.

Dataset Analytics for monet2photo
------------------------------------------
Total images in monet (trainA): 1072
Total images in real (trainB): 6287
Average dimensions for monet images: 256.00 x 256.00
Average dimensions for real images: 256.00 x 256.00
Image dimensions range for monet images: [256 256] to [256 256]
Image dimensions range for real images: [256 256] to [256 256]

Balancing the Dataset Across Domains

Define paths to the training data for monet (trainA) and real (trainB) images

trainA_path = './datasets/monet2photo/trainA' # monet images

trainB_path = './datasets/monet2photo/trainB' # real images

import shutil
from pathlib import Path

# Define new folder paths for balanced dataset
balanced_data_path = Path('./datasets/monet2photo_balanced')
trainA_balanced_path = balanced_data_path / 'trainA'
trainB_balanced_path = balanced_data_path / 'trainB'

# Create directories if they don't exist
trainA_balanced_path.mkdir(parents=True, exist_ok=True)
trainB_balanced_path.mkdir(parents=True, exist_ok=True)

def balance_data(source_path, dest_path, num_samples=500):
    # Get a random sample of file names from the source directory
    sampled_files = random.sample(os.listdir(source_path), num_samples)

    # Copy sampled files to the destination directory
    for file_name in sampled_files:
        source_file = os.path.join(source_path, file_name)
        dest_file = os.path.join(dest_path, file_name)
        shutil.copy(source_file, dest_file)

# Balance the data by copying 500 images from each domain to the new directories
balance_data(trainA_path, trainA_balanced_path, num_samples=500)
balance_data(trainB_path, trainB_balanced_path, num_samples=500)


print("Balanced dataset created with 500 images in each domain (monet and real).")
print(f"Balanced data is stored in {balanced_data_path}")

# Importance of Balanced Data:

# In image translation tasks, having a balanced dataset across domains is essential for stable and unbiased training.
# When both domains (monet and real) have an equal number of images, the model learns to map between the two more effectively,
# avoiding biases that may arise if one domain has significantly more images than the other.
# This balanced subset will also speed up the training process, making it ideal for testing and experimentation.

Balanced dataset created with 500 images in each domain (monet and real).
Balanced data is stored in datasets/monet2photo_balanced

from pathlib import Path

# Check if the directory exists
print("Does the balanced_data_path exist?:", balanced_data_path.exists())

# List files in the directory
for path in balanced_data_path.iterdir():
    print(path)

Does the balanced_data_path exist?: True
datasets/monet2photo_balanced/trainA
datasets/monet2photo_balanced/trainB

import os
print(os.listdir('/content'))  # List files and folders in the /content directory

['.config', 'pytorch-CycleGAN-and-pix2pix', 'sample_data']

Training CycleGAN on the Balanced monet2photo Dataset

# Training CycleGAN from Scratch:
# We’ll train CycleGAN from scratch on the monet2photo dataset to learn the mapping between unpaired monet and real images.

# Steps for Training CycleGAN:

# Step 1: Pass a monet image through G -> generates real-style image
# Step 2: Pass generated real image through F -> reconstructs monet image

# Step 3: Pass a real image through F -> generates monet-style image
# Step 4: Pass generated monet image through G -> reconstructs real image

# Step 5: DA and DB distinguish real vs. generated images in both domains to improve realism

# Training Command:
!python train.py \
    --dataroot ./datasets/monet2photo_balanced \
    --name monet2photo \
    --model cycle_gan \
    --display_id -1 \
    --n_epochs 10 \
    --use_wandb \
    --batch_size 4 \
    --verbose



# Explanation:
# - `--dataroot`: Specifies the dataset path.
# - `--name`: Sets the experiment name (useful for organizing logs and models).
# - `--model`: Specifies the 'cycle_gan' model.
# - `--display_id -1`: Disables intermediate display (useful in Colab).

# Running this command will start CycleGAN training from scratch on the balanced monet2photo dataset.

----------------- Options ---------------
               batch_size: 4                             	[default: 1]
                    beta1: 0.5                           
          checkpoints_dir: ./checkpoints                 
           continue_train: False                         
                crop_size: 256                           
                 dataroot: ./datasets/monet2photo_balanced	[default: None]
             dataset_mode: unaligned                     
                direction: AtoB                          
              display_env: main                          
             display_freq: 400                           
               display_id: -1                            	[default: 1]
            display_ncols: 4                             
             display_port: 8097                          
           display_server: http://localhost              
          display_winsize: 256                           
                    epoch: latest                        
              epoch_count: 1                             
                 gan_mode: lsgan                         
                  gpu_ids: 0                             
                init_gain: 0.02                          
                init_type: normal                        
                 input_nc: 3                             
                  isTrain: True                          	[default: None]
                 lambda_A: 10.0                          
                 lambda_B: 10.0                          
          lambda_identity: 0.5                           
                load_iter: 0                             	[default: 0]
                load_size: 286                           
                       lr: 0.0002                        
           lr_decay_iters: 50                            
                lr_policy: linear                        
         max_dataset_size: inf                           
                    model: cycle_gan                     
                 n_epochs: 10                            	[default: 100]
           n_epochs_decay: 100                           
               n_layers_D: 3                             
                     name: monet2photo                   	[default: experiment_name]
                      ndf: 64                            
                     netD: basic                         
                     netG: resnet_9blocks                
                      ngf: 64                            
               no_dropout: True                          
                  no_flip: False                         
                  no_html: False                         
                     norm: instance                      
              num_threads: 4                             
                output_nc: 3                             
                    phase: train                         
                pool_size: 50                            
               preprocess: resize_and_crop               
               print_freq: 100                           
             save_by_iter: False                         
          save_epoch_freq: 5                             
         save_latest_freq: 5000                          
           serial_batches: False                         
                   suffix:                               
         update_html_freq: 1000                          
                use_wandb: True                          	[default: False]
                  verbose: True                          	[default: False]
       wandb_project_name: CycleGAN-and-pix2pix          
----------------- End -------------------
dataset [UnalignedDataset] was created
The number of training images = 500
initialize network with normal
initialize network with normal
initialize network with normal
initialize network with normal
model [CycleGANModel] was created
---------- Networks initialized -------------
DataParallel(
  (module): ResnetGenerator(
    (model): Sequential(
      (0): ReflectionPad2d((3, 3, 3, 3))
      (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
      (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (6): ReLU(inplace=True)
      (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (9): ReLU(inplace=True)
      (10): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (11): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (12): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (13): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (14): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (15): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (16): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (17): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (18): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
      (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (21): ReLU(inplace=True)
      (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
      (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (24): ReLU(inplace=True)
      (25): ReflectionPad2d((3, 3, 3, 3))
      (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
      (27): Tanh()
    )
  )
)
[Network G_A] Total number of parameters : 11.378 M
DataParallel(
  (module): ResnetGenerator(
    (model): Sequential(
      (0): ReflectionPad2d((3, 3, 3, 3))
      (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
      (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (3): ReLU(inplace=True)
      (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (6): ReLU(inplace=True)
      (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (9): ReLU(inplace=True)
      (10): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (11): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (12): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (13): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (14): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (15): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (16): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (17): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (18): ResnetBlock(
        (conv_block): Sequential(
          (0): ReflectionPad2d((1, 1, 1, 1))
          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
          (3): ReLU(inplace=True)
          (4): ReflectionPad2d((1, 1, 1, 1))
          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        )
      )
      (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
      (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (21): ReLU(inplace=True)
      (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
      (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (24): ReLU(inplace=True)
      (25): ReflectionPad2d((3, 3, 3, 3))
      (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
      (27): Tanh()
    )
  )
)
[Network G_B] Total number of parameters : 11.378 M
DataParallel(
  (module): NLayerDiscriminator(
    (model): Sequential(
      (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (1): LeakyReLU(negative_slope=0.2, inplace=True)
      (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (4): LeakyReLU(negative_slope=0.2, inplace=True)
      (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (7): LeakyReLU(negative_slope=0.2, inplace=True)
      (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
      (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (10): LeakyReLU(negative_slope=0.2, inplace=True)
      (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    )
  )
)
[Network D_A] Total number of parameters : 2.765 M
DataParallel(
  (module): NLayerDiscriminator(
    (model): Sequential(
      (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (1): LeakyReLU(negative_slope=0.2, inplace=True)
      (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (4): LeakyReLU(negative_slope=0.2, inplace=True)
      (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (7): LeakyReLU(negative_slope=0.2, inplace=True)
      (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
      (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      (10): LeakyReLU(negative_slope=0.2, inplace=True)
      (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    )
  )
)
[Network D_B] Total number of parameters : 2.765 M
-----------------------------------------------
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create web directory ./checkpoints/monet2photo/web...
/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:224: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  warnings.warn(
learning rate 0.0002000 -> 0.0002000
(epoch: 1, iters: 100, time: 0.539, data: 0.183) D_A: 0.506 G_A: 0.523 cycle_A: 3.675 idt_A: 1.652 D_B: 0.612 G_B: 0.477 cycle_B: 3.487 idt_B: 1.829 
(epoch: 1, iters: 200, time: 0.549, data: 0.003) D_A: 0.362 G_A: 0.500 cycle_A: 2.965 idt_A: 2.063 D_B: 0.529 G_B: 0.531 cycle_B: 4.339 idt_B: 1.370 
(epoch: 1, iters: 300, time: 0.559, data: 0.003) D_A: 0.405 G_A: 0.385 cycle_A: 2.626 idt_A: 1.325 D_B: 0.324 G_B: 0.288 cycle_B: 2.862 idt_B: 1.243 
(epoch: 1, iters: 400, time: 0.918, data: 0.003) D_A: 0.334 G_A: 0.388 cycle_A: 2.770 idt_A: 1.354 D_B: 0.311 G_B: 0.341 cycle_B: 3.159 idt_B: 1.250 
(epoch: 1, iters: 500, time: 0.567, data: 0.003) D_A: 0.332 G_A: 0.355 cycle_A: 2.882 idt_A: 1.424 D_B: 0.249 G_B: 0.350 cycle_B: 3.924 idt_B: 1.328 
End of epoch 1 / 110 	 Time Taken: 235 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 2, iters: 100, time: 0.570, data: 0.172) D_A: 0.268 G_A: 0.316 cycle_A: 3.143 idt_A: 1.057 D_B: 0.268 G_B: 0.370 cycle_B: 2.254 idt_B: 1.582 
(epoch: 2, iters: 200, time: 0.572, data: 0.003) D_A: 0.282 G_A: 0.329 cycle_A: 3.059 idt_A: 1.399 D_B: 0.286 G_B: 0.296 cycle_B: 4.200 idt_B: 1.590 
(epoch: 2, iters: 300, time: 0.723, data: 0.003) D_A: 0.234 G_A: 0.395 cycle_A: 3.222 idt_A: 1.472 D_B: 0.264 G_B: 0.295 cycle_B: 3.731 idt_B: 1.523 
(epoch: 2, iters: 400, time: 0.572, data: 0.003) D_A: 0.323 G_A: 0.355 cycle_A: 2.477 idt_A: 1.726 D_B: 0.240 G_B: 0.368 cycle_B: 4.323 idt_B: 1.092 
(epoch: 2, iters: 500, time: 0.574, data: 0.003) D_A: 0.259 G_A: 0.230 cycle_A: 2.595 idt_A: 1.526 D_B: 0.251 G_B: 0.344 cycle_B: 3.390 idt_B: 1.380 
End of epoch 2 / 110 	 Time Taken: 233 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 3, iters: 100, time: 0.574, data: 0.138) D_A: 0.251 G_A: 0.332 cycle_A: 2.424 idt_A: 0.846 D_B: 0.311 G_B: 0.516 cycle_B: 1.922 idt_B: 1.204 
(epoch: 3, iters: 200, time: 0.735, data: 0.003) D_A: 0.361 G_A: 0.387 cycle_A: 2.717 idt_A: 1.504 D_B: 0.381 G_B: 0.453 cycle_B: 3.267 idt_B: 1.284 
(epoch: 3, iters: 300, time: 0.574, data: 0.003) D_A: 0.225 G_A: 0.308 cycle_A: 2.953 idt_A: 1.412 D_B: 0.274 G_B: 0.309 cycle_B: 3.060 idt_B: 1.294 
(epoch: 3, iters: 400, time: 0.574, data: 0.003) D_A: 0.264 G_A: 0.418 cycle_A: 2.079 idt_A: 0.890 D_B: 0.213 G_B: 0.324 cycle_B: 2.187 idt_B: 1.015 
(epoch: 3, iters: 500, time: 0.576, data: 0.003) D_A: 0.235 G_A: 0.439 cycle_A: 3.008 idt_A: 0.899 D_B: 0.179 G_B: 0.396 cycle_B: 2.536 idt_B: 1.393 
End of epoch 3 / 110 	 Time Taken: 234 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 4, iters: 100, time: 0.735, data: 0.153) D_A: 0.257 G_A: 0.334 cycle_A: 2.122 idt_A: 0.864 D_B: 0.209 G_B: 0.416 cycle_B: 1.653 idt_B: 1.021 
(epoch: 4, iters: 200, time: 0.576, data: 0.004) D_A: 0.225 G_A: 0.288 cycle_A: 2.075 idt_A: 1.288 D_B: 0.319 G_B: 0.487 cycle_B: 2.651 idt_B: 0.945 
(epoch: 4, iters: 300, time: 0.575, data: 0.003) D_A: 0.345 G_A: 0.441 cycle_A: 1.813 idt_A: 0.990 D_B: 0.198 G_B: 0.368 cycle_B: 2.062 idt_B: 0.800 
(epoch: 4, iters: 400, time: 0.576, data: 0.003) D_A: 0.277 G_A: 0.443 cycle_A: 1.946 idt_A: 1.271 D_B: 0.319 G_B: 0.373 cycle_B: 2.898 idt_B: 0.861 
(epoch: 4, iters: 500, time: 0.657, data: 0.003) D_A: 0.326 G_A: 0.288 cycle_A: 2.800 idt_A: 1.010 D_B: 0.206 G_B: 0.481 cycle_B: 2.529 idt_B: 1.314 
End of epoch 4 / 110 	 Time Taken: 235 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 5, iters: 100, time: 0.574, data: 0.131) D_A: 0.225 G_A: 0.331 cycle_A: 2.213 idt_A: 1.138 D_B: 0.232 G_B: 0.412 cycle_B: 1.885 idt_B: 1.147 
(epoch: 5, iters: 200, time: 0.575, data: 0.003) D_A: 0.268 G_A: 0.462 cycle_A: 3.056 idt_A: 1.334 D_B: 0.142 G_B: 0.390 cycle_B: 2.933 idt_B: 1.327 
(epoch: 5, iters: 300, time: 0.575, data: 0.003) D_A: 0.225 G_A: 0.261 cycle_A: 2.068 idt_A: 1.181 D_B: 0.273 G_B: 0.415 cycle_B: 2.504 idt_B: 0.875 
(epoch: 5, iters: 400, time: 0.736, data: 0.003) D_A: 0.236 G_A: 0.352 cycle_A: 2.338 idt_A: 1.095 D_B: 0.182 G_B: 0.416 cycle_B: 2.338 idt_B: 1.137 
(epoch: 5, iters: 500, time: 0.575, data: 0.004) D_A: 0.195 G_A: 0.484 cycle_A: 2.603 idt_A: 1.190 D_B: 0.200 G_B: 0.532 cycle_B: 2.500 idt_B: 1.093 
saving the model at the end of epoch 5, iters 2500
End of epoch 5 / 110 	 Time Taken: 235 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 6, iters: 100, time: 0.575, data: 0.147) D_A: 0.236 G_A: 0.325 cycle_A: 2.587 idt_A: 1.112 D_B: 0.190 G_B: 0.390 cycle_B: 2.710 idt_B: 1.175 
(epoch: 6, iters: 200, time: 0.574, data: 0.003) D_A: 0.251 G_A: 0.312 cycle_A: 2.336 idt_A: 1.113 D_B: 0.187 G_B: 0.436 cycle_B: 2.628 idt_B: 1.070 
(epoch: 6, iters: 300, time: 0.733, data: 0.003) D_A: 0.235 G_A: 0.304 cycle_A: 2.216 idt_A: 0.884 D_B: 0.316 G_B: 0.176 cycle_B: 2.026 idt_B: 1.091 
(epoch: 6, iters: 400, time: 0.574, data: 0.003) D_A: 0.225 G_A: 0.444 cycle_A: 2.125 idt_A: 1.042 D_B: 0.229 G_B: 0.447 cycle_B: 2.115 idt_B: 1.010 
(epoch: 6, iters: 500, time: 0.575, data: 0.003) D_A: 0.193 G_A: 0.350 cycle_A: 2.010 idt_A: 0.941 D_B: 0.231 G_B: 0.370 cycle_B: 2.209 idt_B: 0.871 
End of epoch 6 / 110 	 Time Taken: 234 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 7, iters: 100, time: 0.574, data: 0.128) D_A: 0.184 G_A: 0.275 cycle_A: 2.056 idt_A: 1.234 D_B: 0.191 G_B: 0.433 cycle_B: 2.723 idt_B: 0.997 
(epoch: 7, iters: 200, time: 0.739, data: 0.003) D_A: 0.168 G_A: 0.437 cycle_A: 2.181 idt_A: 1.224 D_B: 0.198 G_B: 0.544 cycle_B: 3.090 idt_B: 1.056 
(epoch: 7, iters: 300, time: 0.573, data: 0.003) D_A: 0.251 G_A: 0.320 cycle_A: 1.720 idt_A: 0.725 D_B: 0.304 G_B: 0.321 cycle_B: 1.645 idt_B: 0.815 
(epoch: 7, iters: 400, time: 0.574, data: 0.003) D_A: 0.241 G_A: 0.236 cycle_A: 2.000 idt_A: 0.856 D_B: 0.161 G_B: 0.445 cycle_B: 1.715 idt_B: 0.983 
(epoch: 7, iters: 500, time: 0.573, data: 0.003) D_A: 0.372 G_A: 0.443 cycle_A: 1.919 idt_A: 1.098 D_B: 0.243 G_B: 0.372 cycle_B: 2.209 idt_B: 0.930 
End of epoch 7 / 110 	 Time Taken: 234 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 8, iters: 100, time: 0.732, data: 0.119) D_A: 0.201 G_A: 0.557 cycle_A: 1.737 idt_A: 1.069 D_B: 0.269 G_B: 0.425 cycle_B: 2.619 idt_B: 0.900 
(epoch: 8, iters: 200, time: 0.574, data: 0.003) D_A: 0.218 G_A: 0.309 cycle_A: 1.745 idt_A: 0.850 D_B: 0.203 G_B: 0.397 cycle_B: 1.974 idt_B: 0.850 
(epoch: 8, iters: 300, time: 0.573, data: 0.003) D_A: 0.276 G_A: 0.653 cycle_A: 2.245 idt_A: 0.707 D_B: 0.195 G_B: 0.329 cycle_B: 1.512 idt_B: 0.986 
(epoch: 8, iters: 400, time: 0.575, data: 0.003) D_A: 0.184 G_A: 0.342 cycle_A: 2.367 idt_A: 1.346 D_B: 0.183 G_B: 0.590 cycle_B: 2.863 idt_B: 1.085 
(epoch: 8, iters: 500, time: 0.667, data: 0.003) D_A: 0.219 G_A: 0.348 cycle_A: 2.513 idt_A: 0.774 D_B: 0.188 G_B: 0.660 cycle_B: 1.567 idt_B: 1.120 
End of epoch 8 / 110 	 Time Taken: 234 sec
learning rate 0.0002000 -> 0.0002000
(epoch: 9, iters: 100, time: 0.574, data: 0.121) D_A: 0.208 G_A: 0.251 cycle_A: 2.086 idt_A: 1.110 D_B: 0.265 G_B: 0.382 cycle_B: 2.376 idt_B: 0.953 
(epoch: 9, iters: 200, time: 0.572, data: 0.003) D_A: 0.213 G_A: 0.410 cycle_A: 3.049 idt_A: 0.977 D_B: 0.200 G_B: 0.309 cycle_B: 2.552 idt_B: 1.491 
(epoch: 9, iters: 300, time: 0.573, data: 0.003) D_A: 0.171 G_A: 0.429 cycle_A: 2.610 idt_A: 1.042 D_B: 0.342 G_B: 0.364 cycle_B: 2.253 idt_B: 1.268 
(epoch: 9, iters: 400, time: 0.741, data: 0.003) D_A: 0.543 G_A: 0.725 cycle_A: 2.455 idt_A: 1.191 D_B: 0.178 G_B: 0.344 cycle_B: 2.576 idt_B: 0.990 
(epoch: 9, iters: 500, time: 0.575, data: 0.003) D_A: 0.263 G_A: 0.406 cycle_A: 1.759 idt_A: 0.738 D_B: 0.218 G_B: 0.527 cycle_B: 1.650 idt_B: 0.848 
End of epoch 9 / 110 	 Time Taken: 234 sec
learning rate 0.0002000 -> 0.0001980
(epoch: 10, iters: 100, time: 0.575, data: 0.124) D_A: 0.248 G_A: 0.485 cycle_A: 2.099 idt_A: 0.890 D_B: 0.204 G_B: 0.332 cycle_B: 2.037 idt_B: 1.069 
(epoch: 10, iters: 200, time: 0.575, data: 0.003) D_A: 0.185 G_A: 0.521 cycle_A: 2.639 idt_A: 0.691 D_B: 0.165 G_B: 0.450 cycle_B: 1.515 idt_B: 1.190 
(epoch: 10, iters: 300, time: 0.738, data: 0.003) D_A: 0.161 G_A: 0.309 cycle_A: 2.340 idt_A: 0.620 D_B: 0.244 G_B: 0.290 cycle_B: 1.554 idt_B: 1.078 
(epoch: 10, iters: 400, time: 0.576, data: 0.003) D_A: 0.230 G_A: 0.387 cycle_A: 1.726 idt_A: 0.944 D_B: 0.255 G_B: 0.432 cycle_B: 2.105 idt_B: 0.803 
(epoch: 10, iters: 500, time: 0.574, data: 0.003) D_A: 0.236 G_A: 0.459 cycle_A: 2.065 idt_A: 0.917 D_B: 0.130 G_B: 0.407 cycle_B: 1.745 idt_B: 1.025 
saving the latest model (epoch 10, total_iters 5000)
saving the model at the end of epoch 10, iters 5000
End of epoch 10 / 110 	 Time Taken: 235 sec
learning rate 0.0001980 -> 0.0001960
(epoch: 11, iters: 100, time: 0.575, data: 0.157) D_A: 0.176 G_A: 0.401 cycle_A: 2.194 idt_A: 0.955 D_B: 0.254 G_B: 0.593 cycle_B: 2.206 idt_B: 1.091 
(epoch: 11, iters: 200, time: 0.794, data: 0.003) D_A: 0.281 G_A: 0.253 cycle_A: 2.074 idt_A: 1.009 D_B: 0.189 G_B: 0.398 cycle_B: 2.367 idt_B: 1.046 
(epoch: 11, iters: 300, time: 0.574, data: 0.003) D_A: 0.450 G_A: 0.717 cycle_A: 1.958 idt_A: 0.698 D_B: 0.108 G_B: 0.398 cycle_B: 1.437 idt_B: 0.801 
(epoch: 11, iters: 400, time: 0.576, data: 0.004) D_A: 0.386 G_A: 0.472 cycle_A: 2.020 idt_A: 0.999 D_B: 0.253 G_B: 0.509 cycle_B: 2.217 idt_B: 0.932 
(epoch: 11, iters: 500, time: 0.575, data: 0.003) D_A: 0.168 G_A: 0.448 cycle_A: 1.708 idt_A: 0.963 D_B: 0.182 G_B: 0.451 cycle_B: 2.427 idt_B: 0.882 
End of epoch 11 / 110 	 Time Taken: 235 sec
learning rate 0.0001960 -> 0.0001941
(epoch: 12, iters: 100, time: 0.756, data: 0.161) D_A: 0.199 G_A: 0.398 cycle_A: 3.487 idt_A: 1.018 D_B: 0.159 G_B: 0.433 cycle_B: 2.329 idt_B: 1.791 
(epoch: 12, iters: 200, time: 0.575, data: 0.003) D_A: 0.206 G_A: 0.605 cycle_A: 1.942 idt_A: 0.613 D_B: 0.280 G_B: 0.516 cycle_B: 1.520 idt_B: 0.966 
(epoch: 12, iters: 300, time: 0.575, data: 0.003) D_A: 0.235 G_A: 0.514 cycle_A: 2.091 idt_A: 0.888 D_B: 0.172 G_B: 0.275 cycle_B: 1.854 idt_B: 0.979 
(epoch: 12, iters: 400, time: 0.576, data: 0.002) D_A: 0.160 G_A: 0.382 cycle_A: 2.420 idt_A: 0.990 D_B: 0.239 G_B: 0.657 cycle_B: 2.144 idt_B: 1.132 
(epoch: 12, iters: 500, time: 0.672, data: 0.003) D_A: 0.164 G_A: 0.594 cycle_A: 1.813 idt_A: 0.648 D_B: 0.212 G_B: 0.665 cycle_B: 1.484 idt_B: 0.940 
End of epoch 12 / 110 	 Time Taken: 235 sec
learning rate 0.0001941 -> 0.0001921
(epoch: 13, iters: 100, time: 0.574, data: 0.151) D_A: 0.165 G_A: 0.364 cycle_A: 1.695 idt_A: 0.735 D_B: 0.212 G_B: 0.331 cycle_B: 1.632 idt_B: 0.922 
(epoch: 13, iters: 200, time: 0.576, data: 0.003) D_A: 0.246 G_A: 0.322 cycle_A: 2.336 idt_A: 1.058 D_B: 0.218 G_B: 0.350 cycle_B: 3.000 idt_B: 1.041 
(epoch: 13, iters: 300, time: 0.574, data: 0.003) D_A: 0.161 G_A: 0.424 cycle_A: 2.504 idt_A: 0.930 D_B: 0.238 G_B: 0.156 cycle_B: 2.011 idt_B: 0.812 
(epoch: 13, iters: 400, time: 0.745, data: 0.003) D_A: 0.184 G_A: 0.274 cycle_A: 2.037 idt_A: 0.802 D_B: 0.264 G_B: 0.326 cycle_B: 1.726 idt_B: 1.147 
(epoch: 13, iters: 500, time: 0.575, data: 0.003) D_A: 0.180 G_A: 0.535 cycle_A: 1.921 idt_A: 0.836 D_B: 0.113 G_B: 0.361 cycle_B: 1.898 idt_B: 0.910 
End of epoch 13 / 110 	 Time Taken: 234 sec
learning rate 0.0001921 -> 0.0001901
(epoch: 14, iters: 100, time: 0.575, data: 0.136) D_A: 0.143 G_A: 0.327 cycle_A: 1.923 idt_A: 0.941 D_B: 0.131 G_B: 0.366 cycle_B: 1.882 idt_B: 0.809 
(epoch: 14, iters: 200, time: 0.574, data: 0.003) D_A: 0.352 G_A: 0.415 cycle_A: 1.896 idt_A: 0.646 D_B: 0.187 G_B: 0.368 cycle_B: 1.305 idt_B: 0.975 
(epoch: 14, iters: 300, time: 0.739, data: 0.003) D_A: 0.367 G_A: 0.789 cycle_A: 1.635 idt_A: 0.992 D_B: 0.292 G_B: 0.463 cycle_B: 2.071 idt_B: 0.882 
(epoch: 14, iters: 400, time: 0.575, data: 0.004) D_A: 0.215 G_A: 0.700 cycle_A: 1.553 idt_A: 0.842 D_B: 0.255 G_B: 0.310 cycle_B: 2.023 idt_B: 0.744 
(epoch: 14, iters: 500, time: 0.574, data: 0.003) D_A: 0.188 G_A: 0.463 cycle_A: 1.963 idt_A: 0.759 D_B: 0.159 G_B: 0.636 cycle_B: 1.877 idt_B: 0.946 
End of epoch 14 / 110 	 Time Taken: 234 sec
learning rate 0.0001901 -> 0.0001881
(epoch: 15, iters: 100, time: 0.575, data: 0.158) D_A: 0.198 G_A: 0.425 cycle_A: 1.518 idt_A: 0.790 D_B: 0.176 G_B: 0.271 cycle_B: 1.504 idt_B: 0.694 
(epoch: 15, iters: 200, time: 0.750, data: 0.004) D_A: 0.190 G_A: 0.545 cycle_A: 1.744 idt_A: 0.858 D_B: 0.204 G_B: 0.497 cycle_B: 2.142 idt_B: 0.882 
(epoch: 15, iters: 300, time: 0.575, data: 0.003) D_A: 0.234 G_A: 0.486 cycle_A: 2.004 idt_A: 0.739 D_B: 0.202 G_B: 0.335 cycle_B: 1.434 idt_B: 0.900 
(epoch: 15, iters: 400, time: 0.575, data: 0.003) D_A: 0.210 G_A: 0.357 cycle_A: 1.797 idt_A: 0.969 D_B: 0.158 G_B: 0.415 cycle_B: 2.015 idt_B: 0.852 
(epoch: 15, iters: 500, time: 0.577, data: 0.003) D_A: 0.163 G_A: 0.229 cycle_A: 3.087 idt_A: 0.782 D_B: 0.182 G_B: 0.385 cycle_B: 2.281 idt_B: 1.229 
saving the model at the end of epoch 15, iters 7500
End of epoch 15 / 110 	 Time Taken: 235 sec
learning rate 0.0001881 -> 0.0001861
(epoch: 16, iters: 100, time: 0.745, data: 0.135) D_A: 0.214 G_A: 0.343 cycle_A: 1.890 idt_A: 0.869 D_B: 0.230 G_B: 0.172 cycle_B: 2.105 idt_B: 0.901 
(epoch: 16, iters: 200, time: 0.576, data: 0.003) D_A: 0.153 G_A: 0.377 cycle_A: 1.766 idt_A: 0.644 D_B: 0.158 G_B: 0.653 cycle_B: 1.394 idt_B: 0.949 
(epoch: 16, iters: 300, time: 0.575, data: 0.003) D_A: 0.242 G_A: 0.750 cycle_A: 1.999 idt_A: 0.743 D_B: 0.171 G_B: 0.603 cycle_B: 1.512 idt_B: 0.962 
(epoch: 16, iters: 400, time: 0.575, data: 0.003) D_A: 0.195 G_A: 0.312 cycle_A: 1.777 idt_A: 0.768 D_B: 0.303 G_B: 0.602 cycle_B: 1.681 idt_B: 0.887 
(epoch: 16, iters: 500, time: 0.676, data: 0.003) D_A: 0.196 G_A: 0.381 cycle_A: 1.874 idt_A: 1.220 D_B: 0.257 G_B: 0.289 cycle_B: 2.680 idt_B: 0.923 
End of epoch 16 / 110 	 Time Taken: 235 sec
learning rate 0.0001861 -> 0.0001842
(epoch: 17, iters: 100, time: 0.575, data: 0.149) D_A: 0.127 G_A: 0.431 cycle_A: 2.449 idt_A: 0.840 D_B: 0.199 G_B: 0.421 cycle_B: 1.791 idt_B: 1.175 
(epoch: 17, iters: 200, time: 0.576, data: 0.003) D_A: 0.152 G_A: 0.446 cycle_A: 2.001 idt_A: 0.903 D_B: 0.231 G_B: 0.261 cycle_B: 2.117 idt_B: 1.013 
(epoch: 17, iters: 300, time: 0.578, data: 0.003) D_A: 0.244 G_A: 0.607 cycle_A: 1.532 idt_A: 0.934 D_B: 0.170 G_B: 0.313 cycle_B: 2.024 idt_B: 0.770 
(epoch: 17, iters: 400, time: 0.758, data: 0.003) D_A: 0.269 G_A: 0.183 cycle_A: 2.556 idt_A: 0.730 D_B: 0.256 G_B: 0.280 cycle_B: 1.702 idt_B: 1.161 
(epoch: 17, iters: 500, time: 0.575, data: 0.003) D_A: 0.144 G_A: 0.313 cycle_A: 1.940 idt_A: 1.012 D_B: 0.195 G_B: 0.340 cycle_B: 2.377 idt_B: 0.971 
End of epoch 17 / 110 	 Time Taken: 235 sec
learning rate 0.0001842 -> 0.0001822
(epoch: 18, iters: 100, time: 0.575, data: 0.178) D_A: 0.239 G_A: 0.380 cycle_A: 1.792 idt_A: 0.941 D_B: 0.158 G_B: 0.226 cycle_B: 1.727 idt_B: 1.061 
(epoch: 18, iters: 200, time: 0.574, data: 0.003) D_A: 0.172 G_A: 0.424 cycle_A: 1.646 idt_A: 0.795 D_B: 0.169 G_B: 0.306 cycle_B: 1.712 idt_B: 0.719 
(epoch: 18, iters: 300, time: 0.760, data: 0.003) D_A: 0.122 G_A: 0.371 cycle_A: 1.677 idt_A: 1.026 D_B: 0.233 G_B: 0.591 cycle_B: 1.995 idt_B: 0.826 
(epoch: 18, iters: 400, time: 0.575, data: 0.003) D_A: 0.147 G_A: 0.453 cycle_A: 2.049 idt_A: 0.990 D_B: 0.167 G_B: 0.503 cycle_B: 1.876 idt_B: 0.981 
(epoch: 18, iters: 500, time: 0.576, data: 0.003) D_A: 0.251 G_A: 0.493 cycle_A: 2.232 idt_A: 0.751 D_B: 0.194 G_B: 0.418 cycle_B: 1.557 idt_B: 0.899 
End of epoch 18 / 110 	 Time Taken: 235 sec
learning rate 0.0001822 -> 0.0001802
(epoch: 19, iters: 100, time: 0.576, data: 0.152) D_A: 0.241 G_A: 0.467 cycle_A: 1.699 idt_A: 0.660 D_B: 0.148 G_B: 0.630 cycle_B: 1.542 idt_B: 0.738 
(epoch: 19, iters: 200, time: 0.747, data: 0.003) D_A: 0.194 G_A: 0.569 cycle_A: 2.998 idt_A: 1.059 D_B: 0.213 G_B: 0.245 cycle_B: 2.451 idt_B: 1.438 
(epoch: 19, iters: 300, time: 0.575, data: 0.003) D_A: 0.209 G_A: 0.345 cycle_A: 1.845 idt_A: 0.632 D_B: 0.265 G_B: 0.151 cycle_B: 1.434 idt_B: 1.054 
(epoch: 19, iters: 400, time: 0.575, data: 0.003) D_A: 0.174 G_A: 0.466 cycle_A: 1.673 idt_A: 0.656 D_B: 0.174 G_B: 0.473 cycle_B: 1.525 idt_B: 0.900 
(epoch: 19, iters: 500, time: 0.575, data: 0.003) D_A: 0.120 G_A: 0.434 cycle_A: 1.943 idt_A: 0.934 D_B: 0.225 G_B: 0.346 cycle_B: 1.803 idt_B: 0.935 
End of epoch 19 / 110 	 Time Taken: 235 sec
learning rate 0.0001802 -> 0.0001782
(epoch: 20, iters: 100, time: 0.753, data: 0.126) D_A: 0.205 G_A: 0.466 cycle_A: 3.232 idt_A: 0.826 D_B: 0.105 G_B: 0.425 cycle_B: 1.795 idt_B: 1.454 
(epoch: 20, iters: 200, time: 0.575, data: 0.003) D_A: 0.148 G_A: 0.300 cycle_A: 1.594 idt_A: 0.645 D_B: 0.181 G_B: 0.293 cycle_B: 1.462 idt_B: 0.813 
(epoch: 20, iters: 300, time: 0.574, data: 0.003) D_A: 0.281 G_A: 0.615 cycle_A: 2.303 idt_A: 0.642 D_B: 0.145 G_B: 0.327 cycle_B: 1.324 idt_B: 1.099 
(epoch: 20, iters: 400, time: 0.575, data: 0.003) D_A: 0.192 G_A: 0.477 cycle_A: 1.350 idt_A: 0.768 D_B: 0.164 G_B: 0.444 cycle_B: 1.740 idt_B: 0.670 
(epoch: 20, iters: 500, time: 0.684, data: 0.003) D_A: 0.181 G_A: 0.231 cycle_A: 1.757 idt_A: 0.684 D_B: 0.209 G_B: 0.420 cycle_B: 1.491 idt_B: 0.851 
saving the latest model (epoch 20, total_iters 10000)
saving the model at the end of epoch 20, iters 10000
End of epoch 20 / 110 	 Time Taken: 236 sec
learning rate 0.0001782 -> 0.0001762
(epoch: 21, iters: 100, time: 0.574, data: 0.169) D_A: 0.198 G_A: 0.292 cycle_A: 2.015 idt_A: 0.802 D_B: 0.267 G_B: 0.234 cycle_B: 1.773 idt_B: 0.891 
(epoch: 21, iters: 200, time: 0.575, data: 0.003) D_A: 0.214 G_A: 0.218 cycle_A: 2.428 idt_A: 0.867 D_B: 0.102 G_B: 0.519 cycle_B: 1.827 idt_B: 1.077 
(epoch: 21, iters: 300, time: 0.573, data: 0.003) D_A: 0.175 G_A: 0.283 cycle_A: 2.853 idt_A: 0.836 D_B: 0.154 G_B: 0.442 cycle_B: 2.018 idt_B: 1.065 
(epoch: 21, iters: 400, time: 0.759, data: 0.003) D_A: 0.183 G_A: 0.385 cycle_A: 1.443 idt_A: 0.666 D_B: 0.263 G_B: 0.315 cycle_B: 1.345 idt_B: 0.736 
(epoch: 21, iters: 500, time: 0.574, data: 0.003) D_A: 0.283 G_A: 0.386 cycle_A: 1.459 idt_A: 0.668 D_B: 0.309 G_B: 0.303 cycle_B: 1.630 idt_B: 0.699 
End of epoch 21 / 110 	 Time Taken: 234 sec
learning rate 0.0001762 -> 0.0001743
(epoch: 22, iters: 100, time: 0.573, data: 0.140) D_A: 0.183 G_A: 0.520 cycle_A: 1.644 idt_A: 0.637 D_B: 0.213 G_B: 0.451 cycle_B: 1.763 idt_B: 0.857 
(epoch: 22, iters: 200, time: 0.574, data: 0.003) D_A: 0.162 G_A: 0.337 cycle_A: 2.089 idt_A: 0.877 D_B: 0.125 G_B: 0.689 cycle_B: 1.877 idt_B: 0.877 
(epoch: 22, iters: 300, time: 0.750, data: 0.003) D_A: 0.241 G_A: 0.201 cycle_A: 1.681 idt_A: 0.696 D_B: 0.312 G_B: 0.377 cycle_B: 1.583 idt_B: 0.741 
(epoch: 22, iters: 400, time: 0.572, data: 0.003) D_A: 0.218 G_A: 0.411 cycle_A: 1.714 idt_A: 0.773 D_B: 0.228 G_B: 0.138 cycle_B: 1.560 idt_B: 0.896 
(epoch: 22, iters: 500, time: 0.573, data: 0.003) D_A: 0.137 G_A: 0.509 cycle_A: 1.624 idt_A: 0.931 D_B: 0.212 G_B: 0.452 cycle_B: 1.977 idt_B: 0.773 
End of epoch 22 / 110 	 Time Taken: 234 sec
learning rate 0.0001743 -> 0.0001723
(epoch: 23, iters: 100, time: 0.573, data: 0.170) D_A: 0.284 G_A: 0.328 cycle_A: 1.721 idt_A: 0.771 D_B: 0.150 G_B: 0.327 cycle_B: 1.729 idt_B: 0.808 
(epoch: 23, iters: 200, time: 0.817, data: 0.003) D_A: 0.244 G_A: 0.491 cycle_A: 2.077 idt_A: 1.282 D_B: 0.378 G_B: 0.057 cycle_B: 3.010 idt_B: 0.915 
(epoch: 23, iters: 300, time: 0.575, data: 0.003) D_A: 0.223 G_A: 0.259 cycle_A: 1.739 idt_A: 0.837 D_B: 0.218 G_B: 0.662 cycle_B: 2.298 idt_B: 0.798 
(epoch: 23, iters: 400, time: 0.573, data: 0.003) D_A: 0.122 G_A: 0.444 cycle_A: 2.263 idt_A: 1.018 D_B: 0.139 G_B: 0.423 cycle_B: 2.202 idt_B: 1.035 
(epoch: 23, iters: 500, time: 0.574, data: 0.003) D_A: 0.154 G_A: 0.216 cycle_A: 1.565 idt_A: 0.704 D_B: 0.229 G_B: 0.255 cycle_B: 1.450 idt_B: 0.770 
End of epoch 23 / 110 	 Time Taken: 234 sec
learning rate 0.0001723 -> 0.0001703
(epoch: 24, iters: 100, time: 0.753, data: 0.135) D_A: 0.104 G_A: 0.494 cycle_A: 2.420 idt_A: 0.797 D_B: 0.182 G_B: 0.330 cycle_B: 1.787 idt_B: 1.110 
(epoch: 24, iters: 200, time: 0.572, data: 0.002) D_A: 0.154 G_A: 0.307 cycle_A: 1.676 idt_A: 0.699 D_B: 0.236 G_B: 0.473 cycle_B: 1.368 idt_B: 0.774 
(epoch: 24, iters: 300, time: 0.572, data: 0.003) D_A: 0.236 G_A: 0.192 cycle_A: 1.192 idt_A: 0.707 D_B: 0.263 G_B: 0.412 cycle_B: 1.346 idt_B: 0.664 
(epoch: 24, iters: 400, time: 0.574, data: 0.003) D_A: 0.300 G_A: 0.269 cycle_A: 1.493 idt_A: 0.841 D_B: 0.300 G_B: 0.444 cycle_B: 1.629 idt_B: 0.727 
(epoch: 24, iters: 500, time: 0.756, data: 0.003) D_A: 0.254 G_A: 0.190 cycle_A: 1.579 idt_A: 0.638 D_B: 0.180 G_B: 0.311 cycle_B: 1.518 idt_B: 0.614 
End of epoch 24 / 110 	 Time Taken: 235 sec
learning rate 0.0001703 -> 0.0001683
(epoch: 25, iters: 100, time: 0.574, data: 0.137) D_A: 0.204 G_A: 0.501 cycle_A: 1.562 idt_A: 0.585 D_B: 0.246 G_B: 0.385 cycle_B: 1.689 idt_B: 0.807 
(epoch: 25, iters: 200, time: 0.574, data: 0.003) D_A: 0.204 G_A: 0.282 cycle_A: 1.826 idt_A: 0.659 D_B: 0.279 G_B: 0.484 cycle_B: 1.431 idt_B: 0.712 
(epoch: 25, iters: 300, time: 0.574, data: 0.003) D_A: 0.231 G_A: 0.370 cycle_A: 2.057 idt_A: 0.713 D_B: 0.219 G_B: 0.325 cycle_B: 1.937 idt_B: 1.064 
(epoch: 25, iters: 400, time: 0.757, data: 0.003) D_A: 0.233 G_A: 0.368 cycle_A: 1.589 idt_A: 0.661 D_B: 0.234 G_B: 0.339 cycle_B: 1.330 idt_B: 0.801 
(epoch: 25, iters: 500, time: 0.575, data: 0.003) D_A: 0.157 G_A: 0.369 cycle_A: 1.597 idt_A: 0.683 D_B: 0.119 G_B: 0.338 cycle_B: 1.372 idt_B: 0.813 
saving the model at the end of epoch 25, iters 12500
End of epoch 25 / 110 	 Time Taken: 235 sec
learning rate 0.0001683 -> 0.0001663
(epoch: 26, iters: 100, time: 0.573, data: 0.148) D_A: 0.245 G_A: 0.429 cycle_A: 1.317 idt_A: 0.806 D_B: 0.117 G_B: 0.334 cycle_B: 1.574 idt_B: 0.645 
(epoch: 26, iters: 200, time: 0.574, data: 0.003) D_A: 0.172 G_A: 0.559 cycle_A: 2.223 idt_A: 0.588 D_B: 0.176 G_B: 0.313 cycle_B: 1.501 idt_B: 1.127 
(epoch: 26, iters: 300, time: 0.771, data: 0.004) D_A: 0.187 G_A: 0.619 cycle_A: 1.532 idt_A: 0.654 D_B: 0.169 G_B: 0.196 cycle_B: 1.340 idt_B: 0.810 
(epoch: 26, iters: 400, time: 0.576, data: 0.003) D_A: 0.280 G_A: 0.952 cycle_A: 1.295 idt_A: 0.532 D_B: 0.106 G_B: 0.402 cycle_B: 1.058 idt_B: 0.636 
(epoch: 26, iters: 500, time: 0.576, data: 0.003) D_A: 0.177 G_A: 0.401 cycle_A: 2.041 idt_A: 0.877 D_B: 0.224 G_B: 0.362 cycle_B: 1.876 idt_B: 0.918 
End of epoch 26 / 110 	 Time Taken: 234 sec
learning rate 0.0001663 -> 0.0001644
(epoch: 27, iters: 100, time: 0.576, data: 0.154) D_A: 0.153 G_A: 0.262 cycle_A: 1.569 idt_A: 0.916 D_B: 0.308 G_B: 0.422 cycle_B: 1.961 idt_B: 0.747 
(epoch: 27, iters: 200, time: 0.781, data: 0.003) D_A: 0.121 G_A: 0.457 cycle_A: 2.087 idt_A: 0.709 D_B: 0.156 G_B: 0.302 cycle_B: 1.513 idt_B: 0.991 
(epoch: 27, iters: 300, time: 0.574, data: 0.003) D_A: 0.203 G_A: 0.270 cycle_A: 1.269 idt_A: 0.707 D_B: 0.223 G_B: 0.442 cycle_B: 1.479 idt_B: 0.637 
(epoch: 27, iters: 400, time: 0.575, data: 0.003) D_A: 0.127 G_A: 0.484 cycle_A: 1.583 idt_A: 0.697 D_B: 0.117 G_B: 0.434 cycle_B: 1.287 idt_B: 0.707 
(epoch: 27, iters: 500, time: 0.573, data: 0.003) D_A: 0.203 G_A: 0.454 cycle_A: 1.672 idt_A: 0.632 D_B: 0.294 G_B: 0.187 cycle_B: 1.168 idt_B: 0.820 
End of epoch 27 / 110 	 Time Taken: 234 sec
learning rate 0.0001644 -> 0.0001624
(epoch: 28, iters: 100, time: 0.771, data: 0.152) D_A: 0.182 G_A: 0.276 cycle_A: 1.065 idt_A: 0.526 D_B: 0.177 G_B: 0.472 cycle_B: 1.149 idt_B: 0.549 
(epoch: 28, iters: 200, time: 0.575, data: 0.002) D_A: 0.213 G_A: 0.367 cycle_A: 1.388 idt_A: 0.640 D_B: 0.107 G_B: 0.244 cycle_B: 1.491 idt_B: 0.717 
(epoch: 28, iters: 300, time: 0.575, data: 0.003) D_A: 0.216 G_A: 0.452 cycle_A: 2.125 idt_A: 0.625 D_B: 0.216 G_B: 0.267 cycle_B: 1.343 idt_B: 0.926 
(epoch: 28, iters: 400, time: 0.573, data: 0.003) D_A: 0.324 G_A: 0.555 cycle_A: 1.892 idt_A: 0.648 D_B: 0.286 G_B: 0.251 cycle_B: 1.576 idt_B: 0.795 
(epoch: 28, iters: 500, time: 0.685, data: 0.003) D_A: 0.169 G_A: 0.550 cycle_A: 1.518 idt_A: 0.815 D_B: 0.172 G_B: 0.291 cycle_B: 1.530 idt_B: 0.740 
End of epoch 28 / 110 	 Time Taken: 235 sec
learning rate 0.0001624 -> 0.0001604
(epoch: 29, iters: 100, time: 0.574, data: 0.131) D_A: 0.172 G_A: 0.388 cycle_A: 1.373 idt_A: 0.567 D_B: 0.227 G_B: 0.252 cycle_B: 1.321 idt_B: 0.753 
(epoch: 29, iters: 200, time: 0.574, data: 0.003) D_A: 0.233 G_A: 0.274 cycle_A: 1.486 idt_A: 0.777 D_B: 0.259 G_B: 0.165 cycle_B: 1.436 idt_B: 0.692 
(epoch: 29, iters: 300, time: 0.574, data: 0.003) D_A: 0.151 G_A: 0.437 cycle_A: 2.111 idt_A: 0.836 D_B: 0.168 G_B: 0.390 cycle_B: 1.919 idt_B: 0.889 
(epoch: 29, iters: 400, time: 0.762, data: 0.003) D_A: 0.136 G_A: 0.364 cycle_A: 2.264 idt_A: 0.582 D_B: 0.102 G_B: 0.317 cycle_B: 1.219 idt_B: 1.144 
(epoch: 29, iters: 500, time: 0.574, data: 0.003) D_A: 0.296 G_A: 0.171 cycle_A: 1.772 idt_A: 0.652 D_B: 0.218 G_B: 0.317 cycle_B: 1.310 idt_B: 0.906 
End of epoch 29 / 110 	 Time Taken: 234 sec
learning rate 0.0001604 -> 0.0001584
(epoch: 30, iters: 100, time: 0.573, data: 0.142) D_A: 0.271 G_A: 0.137 cycle_A: 1.840 idt_A: 0.612 D_B: 0.233 G_B: 0.630 cycle_B: 1.117 idt_B: 0.808 
(epoch: 30, iters: 200, time: 0.575, data: 0.003) D_A: 0.137 G_A: 0.230 cycle_A: 1.734 idt_A: 0.671 D_B: 0.210 G_B: 0.195 cycle_B: 1.604 idt_B: 0.976 
(epoch: 30, iters: 300, time: 0.772, data: 0.003) D_A: 0.196 G_A: 0.269 cycle_A: 1.338 idt_A: 0.654 D_B: 0.193 G_B: 0.356 cycle_B: 1.539 idt_B: 0.628 
(epoch: 30, iters: 400, time: 0.575, data: 0.003) D_A: 0.145 G_A: 0.352 cycle_A: 1.276 idt_A: 0.683 D_B: 0.142 G_B: 0.408 cycle_B: 1.337 idt_B: 0.675 
(epoch: 30, iters: 500, time: 0.575, data: 0.003) D_A: 0.159 G_A: 0.505 cycle_A: 1.839 idt_A: 1.011 D_B: 0.111 G_B: 0.466 cycle_B: 2.124 idt_B: 0.920 
saving the latest model (epoch 30, total_iters 15000)
saving the model at the end of epoch 30, iters 15000
End of epoch 30 / 110 	 Time Taken: 235 sec
learning rate 0.0001584 -> 0.0001564
(epoch: 31, iters: 100, time: 0.574, data: 0.145) D_A: 0.193 G_A: 0.284 cycle_A: 1.578 idt_A: 0.586 D_B: 0.160 G_B: 0.306 cycle_B: 1.304 idt_B: 0.819 
(epoch: 31, iters: 200, time: 0.771, data: 0.003) D_A: 0.193 G_A: 0.619 cycle_A: 1.639 idt_A: 0.605 D_B: 0.312 G_B: 0.242 cycle_B: 1.808 idt_B: 0.851 
(epoch: 31, iters: 300, time: 0.574, data: 0.003) D_A: 0.232 G_A: 0.403 cycle_A: 1.478 idt_A: 0.543 D_B: 0.215 G_B: 0.262 cycle_B: 1.222 idt_B: 0.596 
(epoch: 31, iters: 400, time: 0.574, data: 0.003) D_A: 0.259 G_A: 0.372 cycle_A: 1.419 idt_A: 0.764 D_B: 0.146 G_B: 0.222 cycle_B: 1.631 idt_B: 0.656 
(epoch: 31, iters: 500, time: 0.575, data: 0.003) D_A: 0.195 G_A: 0.369 cycle_A: 1.360 idt_A: 0.608 D_B: 0.159 G_B: 0.345 cycle_B: 1.501 idt_B: 0.646 
End of epoch 31 / 110 	 Time Taken: 234 sec
learning rate 0.0001564 -> 0.0001545
(epoch: 32, iters: 100, time: 0.819, data: 0.170) D_A: 0.165 G_A: 0.367 cycle_A: 1.418 idt_A: 0.650 D_B: 0.161 G_B: 0.397 cycle_B: 1.199 idt_B: 0.643 
(epoch: 32, iters: 200, time: 0.574, data: 0.003) D_A: 0.231 G_A: 0.176 cycle_A: 1.415 idt_A: 0.612 D_B: 0.178 G_B: 0.355 cycle_B: 1.383 idt_B: 0.571 
(epoch: 32, iters: 300, time: 0.574, data: 0.002) D_A: 0.210 G_A: 0.371 cycle_A: 2.157 idt_A: 0.727 D_B: 0.241 G_B: 0.304 cycle_B: 1.594 idt_B: 0.994 
(epoch: 32, iters: 400, time: 0.574, data: 0.003) D_A: 0.173 G_A: 0.364 cycle_A: 1.380 idt_A: 0.600 D_B: 0.176 G_B: 0.673 cycle_B: 1.491 idt_B: 0.726 
(epoch: 32, iters: 500, time: 0.702, data: 0.003) D_A: 0.123 G_A: 0.374 cycle_A: 1.691 idt_A: 0.682 D_B: 0.166 G_B: 0.253 cycle_B: 1.604 idt_B: 0.740 
End of epoch 32 / 110 	 Time Taken: 235 sec
learning rate 0.0001545 -> 0.0001525
(epoch: 33, iters: 100, time: 0.573, data: 0.172) D_A: 0.170 G_A: 0.332 cycle_A: 3.163 idt_A: 0.728 D_B: 0.193 G_B: 0.218 cycle_B: 1.512 idt_B: 1.262 
(epoch: 33, iters: 200, time: 0.575, data: 0.003) D_A: 0.220 G_A: 0.507 cycle_A: 1.509 idt_A: 0.532 D_B: 0.200 G_B: 0.575 cycle_B: 1.189 idt_B: 0.815 
(epoch: 33, iters: 300, time: 0.575, data: 0.003) D_A: 0.169 G_A: 0.496 cycle_A: 1.154 idt_A: 0.740 D_B: 0.114 G_B: 0.312 cycle_B: 1.336 idt_B: 0.572 
(epoch: 33, iters: 400, time: 0.782, data: 0.003) D_A: 0.171 G_A: 0.444 cycle_A: 1.412 idt_A: 0.539 D_B: 0.214 G_B: 0.265 cycle_B: 1.260 idt_B: 0.743 
(epoch: 33, iters: 500, time: 0.573, data: 0.003) D_A: 0.179 G_A: 0.280 cycle_A: 1.288 idt_A: 0.521 D_B: 0.173 G_B: 0.729 cycle_B: 1.099 idt_B: 0.728 
End of epoch 33 / 110 	 Time Taken: 234 sec
learning rate 0.0001525 -> 0.0001505
(epoch: 34, iters: 100, time: 0.574, data: 0.158) D_A: 0.344 G_A: 0.051 cycle_A: 1.399 idt_A: 0.649 D_B: 0.197 G_B: 0.238 cycle_B: 1.290 idt_B: 0.692 
(epoch: 34, iters: 200, time: 0.573, data: 0.003) D_A: 0.282 G_A: 0.597 cycle_A: 1.524 idt_A: 0.636 D_B: 0.178 G_B: 0.621 cycle_B: 1.324 idt_B: 0.727 
(epoch: 34, iters: 300, time: 0.788, data: 0.003) D_A: 0.282 G_A: 0.496 cycle_A: 1.730 idt_A: 0.610 D_B: 0.176 G_B: 0.293 cycle_B: 1.615 idt_B: 0.888 
(epoch: 34, iters: 400, time: 0.574, data: 0.003) D_A: 0.253 G_A: 0.266 cycle_A: 1.810 idt_A: 0.585 D_B: 0.202 G_B: 0.301 cycle_B: 1.223 idt_B: 0.716 
(epoch: 34, iters: 500, time: 0.575, data: 0.003) D_A: 0.206 G_A: 0.471 cycle_A: 1.655 idt_A: 0.791 D_B: 0.120 G_B: 0.377 cycle_B: 1.643 idt_B: 0.777 
End of epoch 34 / 110 	 Time Taken: 234 sec
learning rate 0.0001505 -> 0.0001485
(epoch: 35, iters: 100, time: 0.573, data: 0.140) D_A: 0.210 G_A: 0.220 cycle_A: 1.337 idt_A: 0.544 D_B: 0.203 G_B: 0.467 cycle_B: 1.293 idt_B: 0.697 
(epoch: 35, iters: 200, time: 0.777, data: 0.003) D_A: 0.287 G_A: 0.443 cycle_A: 1.484 idt_A: 0.795 D_B: 0.145 G_B: 0.412 cycle_B: 1.627 idt_B: 0.892 
(epoch: 35, iters: 300, time: 0.575, data: 0.003) D_A: 0.184 G_A: 0.633 cycle_A: 1.357 idt_A: 0.995 D_B: 0.184 G_B: 0.283 cycle_B: 1.808 idt_B: 0.604 
(epoch: 35, iters: 400, time: 0.574, data: 0.003) D_A: 0.233 G_A: 0.297 cycle_A: 1.675 idt_A: 0.622 D_B: 0.163 G_B: 0.318 cycle_B: 1.369 idt_B: 0.775 
(epoch: 35, iters: 500, time: 0.574, data: 0.003) D_A: 0.227 G_A: 0.550 cycle_A: 3.043 idt_A: 0.708 D_B: 0.099 G_B: 0.333 cycle_B: 1.358 idt_B: 1.254 
saving the model at the end of epoch 35, iters 17500
End of epoch 35 / 110 	 Time Taken: 235 sec
learning rate 0.0001485 -> 0.0001465
(epoch: 36, iters: 100, time: 0.776, data: 0.159) D_A: 0.261 G_A: 0.547 cycle_A: 1.721 idt_A: 0.753 D_B: 0.213 G_B: 0.259 cycle_B: 1.534 idt_B: 0.781 
(epoch: 36, iters: 200, time: 0.575, data: 0.003) D_A: 0.183 G_A: 0.484 cycle_A: 1.378 idt_A: 0.759 D_B: 0.134 G_B: 0.331 cycle_B: 1.868 idt_B: 0.648 
(epoch: 36, iters: 300, time: 0.575, data: 0.003) D_A: 0.385 G_A: 0.758 cycle_A: 1.634 idt_A: 0.740 D_B: 0.392 G_B: 0.047 cycle_B: 1.292 idt_B: 0.879 
(epoch: 36, iters: 400, time: 0.575, data: 0.003) D_A: 0.150 G_A: 0.300 cycle_A: 1.552 idt_A: 0.664 D_B: 0.203 G_B: 0.457 cycle_B: 1.291 idt_B: 0.703 
(epoch: 36, iters: 500, time: 0.698, data: 0.003) D_A: 0.151 G_A: 0.448 cycle_A: 1.439 idt_A: 0.553 D_B: 0.279 G_B: 0.880 cycle_B: 1.252 idt_B: 0.767 
End of epoch 36 / 110 	 Time Taken: 235 sec
learning rate 0.0001465 -> 0.0001446
(epoch: 37, iters: 100, time: 0.575, data: 0.135) D_A: 0.289 G_A: 0.268 cycle_A: 1.837 idt_A: 0.688 D_B: 0.374 G_B: 0.156 cycle_B: 1.355 idt_B: 1.007 
(epoch: 37, iters: 200, time: 0.575, data: 0.003) D_A: 0.170 G_A: 0.374 cycle_A: 1.675 idt_A: 0.899 D_B: 0.250 G_B: 0.504 cycle_B: 1.648 idt_B: 0.983 
(epoch: 37, iters: 300, time: 0.575, data: 0.003) D_A: 0.164 G_A: 0.291 cycle_A: 0.981 idt_A: 0.689 D_B: 0.180 G_B: 0.430 cycle_B: 1.512 idt_B: 0.497 
(epoch: 37, iters: 400, time: 0.781, data: 0.003) D_A: 0.169 G_A: 0.235 cycle_A: 1.578 idt_A: 0.600 D_B: 0.160 G_B: 0.296 cycle_B: 1.361 idt_B: 0.865 
(epoch: 37, iters: 500, time: 0.574, data: 0.003) D_A: 0.153 G_A: 0.299 cycle_A: 1.420 idt_A: 0.694 D_B: 0.174 G_B: 0.497 cycle_B: 1.291 idt_B: 0.797 
End of epoch 37 / 110 	 Time Taken: 234 sec
learning rate 0.0001446 -> 0.0001426
(epoch: 38, iters: 100, time: 0.573, data: 0.169) D_A: 0.130 G_A: 0.467 cycle_A: 1.259 idt_A: 0.486 D_B: 0.207 G_B: 0.364 cycle_B: 1.076 idt_B: 0.626 
(epoch: 38, iters: 200, time: 0.574, data: 0.003) D_A: 0.124 G_A: 0.544 cycle_A: 1.298 idt_A: 0.609 D_B: 0.251 G_B: 0.213 cycle_B: 1.467 idt_B: 0.648 
(epoch: 38, iters: 300, time: 0.784, data: 0.003) D_A: 0.114 G_A: 0.489 cycle_A: 1.354 idt_A: 0.539 D_B: 0.182 G_B: 0.178 cycle_B: 1.163 idt_B: 0.762 
(epoch: 38, iters: 400, time: 0.575, data: 0.003) D_A: 0.188 G_A: 0.478 cycle_A: 1.214 idt_A: 0.913 D_B: 0.271 G_B: 0.249 cycle_B: 1.534 idt_B: 0.540 
(epoch: 38, iters: 500, time: 0.575, data: 0.003) D_A: 0.162 G_A: 0.400 cycle_A: 1.336 idt_A: 0.504 D_B: 0.142 G_B: 0.459 cycle_B: 1.291 idt_B: 0.682 
End of epoch 38 / 110 	 Time Taken: 234 sec
learning rate 0.0001426 -> 0.0001406
(epoch: 39, iters: 100, time: 0.574, data: 0.138) D_A: 0.157 G_A: 0.378 cycle_A: 1.425 idt_A: 0.636 D_B: 0.178 G_B: 0.411 cycle_B: 1.359 idt_B: 0.743 
(epoch: 39, iters: 200, time: 0.844, data: 0.003) D_A: 0.206 G_A: 0.475 cycle_A: 1.714 idt_A: 0.857 D_B: 0.174 G_B: 0.329 cycle_B: 1.454 idt_B: 0.838 
(epoch: 39, iters: 300, time: 0.574, data: 0.003) D_A: 0.199 G_A: 0.225 cycle_A: 1.357 idt_A: 0.734 D_B: 0.133 G_B: 0.362 cycle_B: 1.487 idt_B: 0.688 
(epoch: 39, iters: 400, time: 0.572, data: 0.004) D_A: 0.187 G_A: 0.406 cycle_A: 1.488 idt_A: 0.619 D_B: 0.165 G_B: 0.280 cycle_B: 1.382 idt_B: 0.741 
(epoch: 39, iters: 500, time: 0.576, data: 0.003) D_A: 0.258 G_A: 0.261 cycle_A: 1.298 idt_A: 0.786 D_B: 0.208 G_B: 0.456 cycle_B: 1.492 idt_B: 0.698 
End of epoch 39 / 110 	 Time Taken: 235 sec
learning rate 0.0001406 -> 0.0001386
(epoch: 40, iters: 100, time: 0.782, data: 0.133) D_A: 0.234 G_A: 0.275 cycle_A: 1.466 idt_A: 0.722 D_B: 0.227 G_B: 0.354 cycle_B: 1.434 idt_B: 0.680 
(epoch: 40, iters: 200, time: 0.573, data: 0.003) D_A: 0.165 G_A: 0.263 cycle_A: 1.369 idt_A: 0.594 D_B: 0.257 G_B: 0.413 cycle_B: 1.717 idt_B: 0.786 
(epoch: 40, iters: 300, time: 0.573, data: 0.003) D_A: 0.227 G_A: 0.314 cycle_A: 1.682 idt_A: 0.625 D_B: 0.189 G_B: 0.277 cycle_B: 1.519 idt_B: 0.752 
(epoch: 40, iters: 400, time: 0.573, data: 0.003) D_A: 0.368 G_A: 0.103 cycle_A: 1.566 idt_A: 0.506 D_B: 0.253 G_B: 0.317 cycle_B: 1.171 idt_B: 0.724 
(epoch: 40, iters: 500, time: 0.718, data: 0.003) D_A: 0.275 G_A: 0.222 cycle_A: 1.521 idt_A: 0.538 D_B: 0.146 G_B: 0.409 cycle_B: 1.411 idt_B: 0.743 
saving the latest model (epoch 40, total_iters 20000)
saving the model at the end of epoch 40, iters 20000
End of epoch 40 / 110 	 Time Taken: 236 sec
learning rate 0.0001386 -> 0.0001366
(epoch: 41, iters: 100, time: 0.574, data: 0.175) D_A: 0.232 G_A: 0.696 cycle_A: 1.391 idt_A: 0.778 D_B: 0.189 G_B: 0.244 cycle_B: 1.980 idt_B: 0.637 
(epoch: 41, iters: 200, time: 0.573, data: 0.003) D_A: 0.160 G_A: 0.719 cycle_A: 1.636 idt_A: 0.780 D_B: 0.204 G_B: 0.175 cycle_B: 1.591 idt_B: 0.736 
(epoch: 41, iters: 300, time: 0.575, data: 0.003) D_A: 0.273 G_A: 0.297 cycle_A: 1.335 idt_A: 0.487 D_B: 0.275 G_B: 0.540 cycle_B: 1.266 idt_B: 0.835 
(epoch: 41, iters: 400, time: 0.782, data: 0.003) D_A: 0.188 G_A: 0.346 cycle_A: 1.826 idt_A: 0.553 D_B: 0.134 G_B: 0.270 cycle_B: 1.104 idt_B: 0.765 
(epoch: 41, iters: 500, time: 0.574, data: 0.003) D_A: 0.165 G_A: 0.414 cycle_A: 1.423 idt_A: 0.603 D_B: 0.193 G_B: 0.805 cycle_B: 1.212 idt_B: 0.696 
End of epoch 41 / 110 	 Time Taken: 234 sec
learning rate 0.0001366 -> 0.0001347
(epoch: 42, iters: 100, time: 0.574, data: 0.164) D_A: 0.186 G_A: 0.396 cycle_A: 1.620 idt_A: 0.701 D_B: 0.220 G_B: 0.246 cycle_B: 1.494 idt_B: 0.710 
(epoch: 42, iters: 200, time: 0.574, data: 0.003) D_A: 0.196 G_A: 0.603 cycle_A: 1.356 idt_A: 0.607 D_B: 0.103 G_B: 0.451 cycle_B: 1.353 idt_B: 0.565 
(epoch: 42, iters: 300, time: 0.779, data: 0.003) D_A: 0.237 G_A: 0.363 cycle_A: 1.622 idt_A: 0.625 D_B: 0.241 G_B: 0.102 cycle_B: 1.341 idt_B: 0.740 
(epoch: 42, iters: 400, time: 0.576, data: 0.003) D_A: 0.245 G_A: 0.473 cycle_A: 1.263 idt_A: 0.618 D_B: 0.084 G_B: 0.471 cycle_B: 1.331 idt_B: 0.523 
(epoch: 42, iters: 500, time: 0.575, data: 0.003) D_A: 0.210 G_A: 0.386 cycle_A: 1.564 idt_A: 0.715 D_B: 0.126 G_B: 0.292 cycle_B: 1.631 idt_B: 0.764 
End of epoch 42 / 110 	 Time Taken: 234 sec
learning rate 0.0001347 -> 0.0001327
(epoch: 43, iters: 100, time: 0.575, data: 0.149) D_A: 0.182 G_A: 0.351 cycle_A: 1.606 idt_A: 0.689 D_B: 0.152 G_B: 0.624 cycle_B: 1.385 idt_B: 0.753 
(epoch: 43, iters: 200, time: 0.790, data: 0.003) D_A: 0.153 G_A: 0.378 cycle_A: 1.793 idt_A: 0.564 D_B: 0.130 G_B: 0.373 cycle_B: 1.174 idt_B: 0.794 
(epoch: 43, iters: 300, time: 0.576, data: 0.003) D_A: 0.101 G_A: 0.327 cycle_A: 1.332 idt_A: 0.578 D_B: 0.189 G_B: 0.501 cycle_B: 1.321 idt_B: 0.692 
(epoch: 43, iters: 400, time: 0.574, data: 0.003) D_A: 0.133 G_A: 0.357 cycle_A: 1.801 idt_A: 1.098 D_B: 0.180 G_B: 0.559 cycle_B: 2.194 idt_B: 0.714 
(epoch: 43, iters: 500, time: 0.575, data: 0.003) D_A: 0.142 G_A: 0.301 cycle_A: 1.430 idt_A: 0.793 D_B: 0.243 G_B: 0.332 cycle_B: 1.649 idt_B: 0.742 
End of epoch 43 / 110 	 Time Taken: 234 sec
learning rate 0.0001327 -> 0.0001307
(epoch: 44, iters: 100, time: 0.784, data: 0.164) D_A: 0.253 G_A: 0.269 cycle_A: 1.423 idt_A: 0.597 D_B: 0.140 G_B: 0.253 cycle_B: 1.173 idt_B: 0.802 
(epoch: 44, iters: 200, time: 0.574, data: 0.003) D_A: 0.167 G_A: 0.473 cycle_A: 1.162 idt_A: 0.574 D_B: 0.172 G_B: 0.471 cycle_B: 1.175 idt_B: 0.728 
(epoch: 44, iters: 300, time: 0.574, data: 0.003) D_A: 0.222 G_A: 0.371 cycle_A: 1.524 idt_A: 0.458 D_B: 0.181 G_B: 0.502 cycle_B: 0.949 idt_B: 0.813 
(epoch: 44, iters: 400, time: 0.576, data: 0.003) D_A: 0.166 G_A: 0.286 cycle_A: 1.532 idt_A: 0.721 D_B: 0.198 G_B: 0.242 cycle_B: 1.408 idt_B: 0.838 
(epoch: 44, iters: 500, time: 0.727, data: 0.003) D_A: 0.143 G_A: 0.493 cycle_A: 1.349 idt_A: 0.631 D_B: 0.228 G_B: 0.552 cycle_B: 1.251 idt_B: 0.682 
End of epoch 44 / 110 	 Time Taken: 235 sec
learning rate 0.0001307 -> 0.0001287
(epoch: 45, iters: 100, time: 0.574, data: 0.144) D_A: 0.125 G_A: 0.483 cycle_A: 1.141 idt_A: 0.525 D_B: 0.147 G_B: 0.359 cycle_B: 1.220 idt_B: 0.555 
(epoch: 45, iters: 200, time: 0.574, data: 0.004) D_A: 0.156 G_A: 0.244 cycle_A: 1.712 idt_A: 0.437 D_B: 0.202 G_B: 0.854 cycle_B: 1.141 idt_B: 0.818 
(epoch: 45, iters: 300, time: 0.573, data: 0.003) D_A: 0.148 G_A: 0.347 cycle_A: 1.332 idt_A: 0.544 D_B: 0.191 G_B: 0.256 cycle_B: 1.234 idt_B: 0.614 
(epoch: 45, iters: 400, time: 0.845, data: 0.003) D_A: 0.112 G_A: 0.621 cycle_A: 1.381 idt_A: 0.657 D_B: 0.262 G_B: 0.305 cycle_B: 1.415 idt_B: 0.713 
(epoch: 45, iters: 500, time: 0.574, data: 0.003) D_A: 0.186 G_A: 0.284 cycle_A: 2.138 idt_A: 0.636 D_B: 0.149 G_B: 0.429 cycle_B: 1.415 idt_B: 1.125 
saving the model at the end of epoch 45, iters 22500
End of epoch 45 / 110 	 Time Taken: 235 sec
learning rate 0.0001287 -> 0.0001267
(epoch: 46, iters: 100, time: 0.575, data: 0.147) D_A: 0.244 G_A: 0.328 cycle_A: 1.184 idt_A: 0.648 D_B: 0.205 G_B: 0.481 cycle_B: 1.515 idt_B: 0.607 
(epoch: 46, iters: 200, time: 0.574, data: 0.003) D_A: 0.279 G_A: 0.202 cycle_A: 1.292 idt_A: 0.727 D_B: 0.355 G_B: 0.673 cycle_B: 1.433 idt_B: 0.611 
(epoch: 46, iters: 300, time: 0.800, data: 0.004) D_A: 0.199 G_A: 0.490 cycle_A: 1.621 idt_A: 0.551 D_B: 0.233 G_B: 0.170 cycle_B: 1.275 idt_B: 0.756 
(epoch: 46, iters: 400, time: 0.574, data: 0.004) D_A: 0.126 G_A: 0.517 cycle_A: 1.165 idt_A: 0.585 D_B: 0.187 G_B: 0.286 cycle_B: 1.495 idt_B: 0.566 
(epoch: 46, iters: 500, time: 0.574, data: 0.003) D_A: 0.157 G_A: 0.475 cycle_A: 1.447 idt_A: 0.679 D_B: 0.124 G_B: 0.535 cycle_B: 1.631 idt_B: 0.757 
End of epoch 46 / 110 	 Time Taken: 234 sec
learning rate 0.0001267 -> 0.0001248
(epoch: 47, iters: 100, time: 0.574, data: 0.146) D_A: 0.233 G_A: 0.312 cycle_A: 1.233 idt_A: 0.603 D_B: 0.237 G_B: 0.258 cycle_B: 1.290 idt_B: 0.685 
(epoch: 47, iters: 200, time: 0.813, data: 0.003) D_A: 0.223 G_A: 0.429 cycle_A: 1.483 idt_A: 0.585 D_B: 0.246 G_B: 0.219 cycle_B: 1.202 idt_B: 0.785 
(epoch: 47, iters: 300, time: 0.575, data: 0.003) D_A: 0.106 G_A: 0.672 cycle_A: 1.550 idt_A: 0.613 D_B: 0.228 G_B: 0.347 cycle_B: 1.321 idt_B: 0.888 
(epoch: 47, iters: 400, time: 0.575, data: 0.003) D_A: 0.125 G_A: 0.339 cycle_A: 1.128 idt_A: 0.504 D_B: 0.255 G_B: 0.625 cycle_B: 1.166 idt_B: 0.556 
(epoch: 47, iters: 500, time: 0.576, data: 0.003) D_A: 0.167 G_A: 0.395 cycle_A: 1.076 idt_A: 0.584 D_B: 0.239 G_B: 0.174 cycle_B: 1.244 idt_B: 0.549 
End of epoch 47 / 110 	 Time Taken: 234 sec
learning rate 0.0001248 -> 0.0001228
(epoch: 48, iters: 100, time: 0.802, data: 0.158) D_A: 0.139 G_A: 0.481 cycle_A: 2.021 idt_A: 0.542 D_B: 0.129 G_B: 0.356 cycle_B: 1.015 idt_B: 0.910 
(epoch: 48, iters: 200, time: 0.576, data: 0.003) D_A: 0.170 G_A: 0.406 cycle_A: 1.704 idt_A: 0.537 D_B: 0.174 G_B: 0.475 cycle_B: 1.130 idt_B: 1.147 
(epoch: 48, iters: 300, time: 0.574, data: 0.003) D_A: 0.214 G_A: 0.204 cycle_A: 1.324 idt_A: 0.534 D_B: 0.221 G_B: 0.598 cycle_B: 1.298 idt_B: 0.638 
(epoch: 48, iters: 400, time: 0.573, data: 0.003) D_A: 0.170 G_A: 0.480 cycle_A: 1.458 idt_A: 0.606 D_B: 0.176 G_B: 0.166 cycle_B: 1.272 idt_B: 0.596 
(epoch: 48, iters: 500, time: 0.735, data: 0.003) D_A: 0.179 G_A: 0.556 cycle_A: 1.312 idt_A: 0.582 D_B: 0.188 G_B: 0.253 cycle_B: 1.078 idt_B: 0.791 
End of epoch 48 / 110 	 Time Taken: 235 sec
learning rate 0.0001228 -> 0.0001208
(epoch: 49, iters: 100, time: 0.577, data: 0.141) D_A: 0.125 G_A: 0.631 cycle_A: 1.243 idt_A: 0.675 D_B: 0.116 G_B: 0.381 cycle_B: 1.430 idt_B: 0.658 
(epoch: 49, iters: 200, time: 0.574, data: 0.003) D_A: 0.125 G_A: 0.397 cycle_A: 1.435 idt_A: 0.548 D_B: 0.189 G_B: 0.524 cycle_B: 1.158 idt_B: 0.742 
(epoch: 49, iters: 300, time: 0.574, data: 0.003) D_A: 0.117 G_A: 0.516 cycle_A: 1.486 idt_A: 0.548 D_B: 0.139 G_B: 0.353 cycle_B: 1.316 idt_B: 0.717 
(epoch: 49, iters: 400, time: 0.803, data: 0.003) D_A: 0.206 G_A: 0.702 cycle_A: 1.385 idt_A: 0.652 D_B: 0.185 G_B: 0.626 cycle_B: 1.318 idt_B: 0.792 
(epoch: 49, iters: 500, time: 0.574, data: 0.003) D_A: 0.204 G_A: 0.516 cycle_A: 1.095 idt_A: 0.507 D_B: 0.134 G_B: 0.467 cycle_B: 1.233 idt_B: 0.514 
End of epoch 49 / 110 	 Time Taken: 235 sec
learning rate 0.0001208 -> 0.0001188
(epoch: 50, iters: 100, time: 0.573, data: 0.155) D_A: 0.238 G_A: 0.476 cycle_A: 1.196 idt_A: 0.543 D_B: 0.354 G_B: 0.241 cycle_B: 1.168 idt_B: 0.785 
(epoch: 50, iters: 200, time: 0.575, data: 0.004) D_A: 0.092 G_A: 0.567 cycle_A: 1.421 idt_A: 0.612 D_B: 0.145 G_B: 0.524 cycle_B: 1.511 idt_B: 0.581 
(epoch: 50, iters: 300, time: 0.878, data: 0.003) D_A: 0.141 G_A: 0.437 cycle_A: 1.544 idt_A: 0.527 D_B: 0.187 G_B: 0.347 cycle_B: 1.441 idt_B: 0.641 
(epoch: 50, iters: 400, time: 0.574, data: 0.003) D_A: 0.207 G_A: 0.229 cycle_A: 1.389 idt_A: 0.522 D_B: 0.225 G_B: 0.274 cycle_B: 1.112 idt_B: 0.659 
(epoch: 50, iters: 500, time: 0.575, data: 0.003) D_A: 0.139 G_A: 0.257 cycle_A: 1.362 idt_A: 0.619 D_B: 0.238 G_B: 0.383 cycle_B: 1.317 idt_B: 0.573 
saving the latest model (epoch 50, total_iters 25000)
saving the model at the end of epoch 50, iters 25000
End of epoch 50 / 110 	 Time Taken: 236 sec
learning rate 0.0001188 -> 0.0001168
(epoch: 51, iters: 100, time: 0.575, data: 0.173) D_A: 0.133 G_A: 0.294 cycle_A: 1.145 idt_A: 1.016 D_B: 0.180 G_B: 0.512 cycle_B: 2.062 idt_B: 0.502 
(epoch: 51, iters: 200, time: 0.817, data: 0.004) D_A: 0.204 G_A: 0.293 cycle_A: 1.083 idt_A: 0.462 D_B: 0.213 G_B: 0.610 cycle_B: 1.036 idt_B: 0.596 
(epoch: 51, iters: 300, time: 0.576, data: 0.003) D_A: 0.209 G_A: 0.780 cycle_A: 1.691 idt_A: 0.740 D_B: 0.327 G_B: 0.208 cycle_B: 1.468 idt_B: 0.743 
(epoch: 51, iters: 400, time: 0.575, data: 0.003) D_A: 0.225 G_A: 0.468 cycle_A: 1.085 idt_A: 0.722 D_B: 0.190 G_B: 0.394 cycle_B: 1.310 idt_B: 0.554 
(epoch: 51, iters: 500, time: 0.576, data: 0.003) D_A: 0.175 G_A: 0.348 cycle_A: 1.404 idt_A: 0.427 D_B: 0.187 G_B: 0.637 cycle_B: 1.387 idt_B: 0.681 
End of epoch 51 / 110 	 Time Taken: 235 sec
learning rate 0.0001168 -> 0.0001149
(epoch: 52, iters: 100, time: 0.806, data: 0.153) D_A: 0.186 G_A: 0.420 cycle_A: 1.581 idt_A: 0.524 D_B: 0.095 G_B: 0.339 cycle_B: 1.085 idt_B: 0.711 
(epoch: 52, iters: 200, time: 0.575, data: 0.003) D_A: 0.121 G_A: 0.427 cycle_A: 2.102 idt_A: 0.587 D_B: 0.127 G_B: 0.382 cycle_B: 1.204 idt_B: 1.123 
(epoch: 52, iters: 300, time: 0.575, data: 0.003) D_A: 0.193 G_A: 0.252 cycle_A: 1.437 idt_A: 0.579 D_B: 0.217 G_B: 0.234 cycle_B: 1.151 idt_B: 0.604 
(epoch: 52, iters: 400, time: 0.574, data: 0.003) D_A: 0.214 G_A: 0.146 cycle_A: 1.451 idt_A: 0.542 D_B: 0.202 G_B: 0.184 cycle_B: 1.288 idt_B: 0.794 
(epoch: 52, iters: 500, time: 0.747, data: 0.003) D_A: 0.177 G_A: 0.495 cycle_A: 1.901 idt_A: 0.639 D_B: 0.250 G_B: 0.152 cycle_B: 1.350 idt_B: 0.936 
End of epoch 52 / 110 	 Time Taken: 235 sec
learning rate 0.0001149 -> 0.0001129
(epoch: 53, iters: 100, time: 0.572, data: 0.164) D_A: 0.126 G_A: 0.417 cycle_A: 1.385 idt_A: 0.512 D_B: 0.146 G_B: 0.282 cycle_B: 1.100 idt_B: 0.646 
(epoch: 53, iters: 200, time: 0.576, data: 0.003) D_A: 0.210 G_A: 0.439 cycle_A: 1.102 idt_A: 0.522 D_B: 0.135 G_B: 0.328 cycle_B: 1.146 idt_B: 0.501 
(epoch: 53, iters: 300, time: 0.574, data: 0.003) D_A: 0.208 G_A: 0.513 cycle_A: 1.159 idt_A: 0.600 D_B: 0.084 G_B: 0.427 cycle_B: 1.525 idt_B: 0.660 
(epoch: 53, iters: 400, time: 0.818, data: 0.003) D_A: 0.164 G_A: 0.413 cycle_A: 1.865 idt_A: 0.668 D_B: 0.131 G_B: 0.470 cycle_B: 1.608 idt_B: 0.873 
(epoch: 53, iters: 500, time: 0.572, data: 0.004) D_A: 0.100 G_A: 0.374 cycle_A: 1.087 idt_A: 0.683 D_B: 0.147 G_B: 0.509 cycle_B: 1.171 idt_B: 0.793 
End of epoch 53 / 110 	 Time Taken: 234 sec
learning rate 0.0001129 -> 0.0001109
(epoch: 54, iters: 100, time: 0.573, data: 0.148) D_A: 0.252 G_A: 0.340 cycle_A: 1.678 idt_A: 0.462 D_B: 0.121 G_B: 0.472 cycle_B: 0.938 idt_B: 0.679 
(epoch: 54, iters: 200, time: 0.574, data: 0.003) D_A: 0.119 G_A: 0.474 cycle_A: 1.363 idt_A: 0.585 D_B: 0.212 G_B: 0.567 cycle_B: 1.317 idt_B: 0.649 
(epoch: 54, iters: 300, time: 0.833, data: 0.003) D_A: 0.211 G_A: 0.686 cycle_A: 1.206 idt_A: 0.393 D_B: 0.125 G_B: 0.600 cycle_B: 0.925 idt_B: 0.586 
(epoch: 54, iters: 400, time: 0.576, data: 0.003) D_A: 0.163 G_A: 0.290 cycle_A: 1.138 idt_A: 0.432 D_B: 0.287 G_B: 0.432 cycle_B: 0.907 idt_B: 0.592 
(epoch: 54, iters: 500, time: 0.576, data: 0.003) D_A: 0.166 G_A: 0.327 cycle_A: 1.457 idt_A: 0.820 D_B: 0.154 G_B: 0.563 cycle_B: 1.535 idt_B: 0.637 
End of epoch 54 / 110 	 Time Taken: 235 sec
learning rate 0.0001109 -> 0.0001089
(epoch: 55, iters: 100, time: 0.573, data: 0.160) D_A: 0.209 G_A: 0.345 cycle_A: 1.254 idt_A: 0.494 D_B: 0.201 G_B: 0.315 cycle_B: 0.977 idt_B: 0.593 
(epoch: 55, iters: 200, time: 0.812, data: 0.003) D_A: 0.198 G_A: 0.499 cycle_A: 1.206 idt_A: 0.497 D_B: 0.147 G_B: 0.116 cycle_B: 1.153 idt_B: 0.553 
(epoch: 55, iters: 300, time: 0.572, data: 0.003) D_A: 0.266 G_A: 0.598 cycle_A: 1.199 idt_A: 0.532 D_B: 0.193 G_B: 0.237 cycle_B: 1.329 idt_B: 0.538 
(epoch: 55, iters: 400, time: 0.577, data: 0.003) D_A: 0.142 G_A: 0.253 cycle_A: 1.153 idt_A: 0.555 D_B: 0.191 G_B: 0.479 cycle_B: 1.356 idt_B: 0.597 
(epoch: 55, iters: 500, time: 0.574, data: 0.003) D_A: 0.149 G_A: 0.320 cycle_A: 1.001 idt_A: 0.746 D_B: 0.260 G_B: 0.163 cycle_B: 1.297 idt_B: 0.583 
saving the model at the end of epoch 55, iters 27500
End of epoch 55 / 110 	 Time Taken: 235 sec
learning rate 0.0001089 -> 0.0001069
(epoch: 56, iters: 100, time: 0.880, data: 0.171) D_A: 0.166 G_A: 0.428 cycle_A: 1.609 idt_A: 0.759 D_B: 0.094 G_B: 0.350 cycle_B: 1.397 idt_B: 0.735 
(epoch: 56, iters: 200, time: 0.574, data: 0.003) D_A: 0.096 G_A: 0.311 cycle_A: 1.197 idt_A: 0.653 D_B: 0.221 G_B: 0.489 cycle_B: 1.364 idt_B: 0.570 
(epoch: 56, iters: 300, time: 0.575, data: 0.003) D_A: 0.098 G_A: 0.475 cycle_A: 1.419 idt_A: 0.542 D_B: 0.091 G_B: 0.244 cycle_B: 1.005 idt_B: 0.753 
(epoch: 56, iters: 400, time: 0.574, data: 0.004) D_A: 0.066 G_A: 0.432 cycle_A: 1.744 idt_A: 0.460 D_B: 0.183 G_B: 0.209 cycle_B: 1.109 idt_B: 0.841 
(epoch: 56, iters: 500, time: 0.761, data: 0.003) D_A: 0.157 G_A: 0.574 cycle_A: 1.278 idt_A: 0.413 D_B: 0.170 G_B: 0.509 cycle_B: 0.921 idt_B: 0.607 
End of epoch 56 / 110 	 Time Taken: 236 sec
learning rate 0.0001069 -> 0.0001050
(epoch: 57, iters: 100, time: 0.576, data: 0.149) D_A: 0.131 G_A: 0.292 cycle_A: 1.283 idt_A: 0.461 D_B: 0.207 G_B: 0.185 cycle_B: 1.178 idt_B: 0.566 
(epoch: 57, iters: 200, time: 0.577, data: 0.003) D_A: 0.180 G_A: 0.516 cycle_A: 1.255 idt_A: 0.470 D_B: 0.206 G_B: 0.358 cycle_B: 1.141 idt_B: 0.780 
(epoch: 57, iters: 300, time: 0.575, data: 0.003) D_A: 0.101 G_A: 0.540 cycle_A: 1.442 idt_A: 0.415 D_B: 0.192 G_B: 0.832 cycle_B: 1.489 idt_B: 0.687 
(epoch: 57, iters: 400, time: 0.809, data: 0.003) D_A: 0.221 G_A: 0.567 cycle_A: 1.780 idt_A: 0.534 D_B: 0.150 G_B: 0.237 cycle_B: 1.162 idt_B: 0.801 
(epoch: 57, iters: 500, time: 0.578, data: 0.003) D_A: 0.115 G_A: 0.426 cycle_A: 1.536 idt_A: 0.611 D_B: 0.136 G_B: 0.469 cycle_B: 1.443 idt_B: 0.709 
End of epoch 57 / 110 	 Time Taken: 235 sec
learning rate 0.0001050 -> 0.0001030
(epoch: 58, iters: 100, time: 0.574, data: 0.143) D_A: 0.164 G_A: 0.623 cycle_A: 1.753 idt_A: 0.605 D_B: 0.185 G_B: 0.193 cycle_B: 1.455 idt_B: 0.713 
(epoch: 58, iters: 200, time: 0.575, data: 0.003) D_A: 0.166 G_A: 0.449 cycle_A: 1.537 idt_A: 0.432 D_B: 0.181 G_B: 0.339 cycle_B: 1.059 idt_B: 0.694 
(epoch: 58, iters: 300, time: 0.831, data: 0.003) D_A: 0.126 G_A: 0.555 cycle_A: 1.374 idt_A: 0.422 D_B: 0.164 G_B: 0.801 cycle_B: 0.939 idt_B: 0.709 
(epoch: 58, iters: 400, time: 0.573, data: 0.003) D_A: 0.229 G_A: 0.192 cycle_A: 1.300 idt_A: 0.635 D_B: 0.188 G_B: 0.290 cycle_B: 1.267 idt_B: 0.598 
(epoch: 58, iters: 500, time: 0.574, data: 0.003) D_A: 0.121 G_A: 0.340 cycle_A: 1.102 idt_A: 0.543 D_B: 0.143 G_B: 0.666 cycle_B: 1.453 idt_B: 0.555 
End of epoch 58 / 110 	 Time Taken: 235 sec
learning rate 0.0001030 -> 0.0001010
(epoch: 59, iters: 100, time: 0.575, data: 0.166) D_A: 0.136 G_A: 0.485 cycle_A: 1.133 idt_A: 0.549 D_B: 0.197 G_B: 0.606 cycle_B: 1.164 idt_B: 0.596 
(epoch: 59, iters: 200, time: 0.811, data: 0.003) D_A: 0.115 G_A: 0.414 cycle_A: 1.473 idt_A: 0.462 D_B: 0.131 G_B: 0.370 cycle_B: 1.162 idt_B: 0.535 
(epoch: 59, iters: 300, time: 0.575, data: 0.003) D_A: 0.295 G_A: 0.204 cycle_A: 1.178 idt_A: 0.483 D_B: 0.252 G_B: 0.494 cycle_B: 1.112 idt_B: 0.641 
(epoch: 59, iters: 400, time: 0.574, data: 0.003) D_A: 0.223 G_A: 0.338 cycle_A: 1.333 idt_A: 0.493 D_B: 0.194 G_B: 0.580 cycle_B: 0.956 idt_B: 0.554 
(epoch: 59, iters: 500, time: 0.576, data: 0.003) D_A: 0.225 G_A: 0.405 cycle_A: 1.067 idt_A: 0.481 D_B: 0.218 G_B: 0.489 cycle_B: 1.130 idt_B: 0.643 
End of epoch 59 / 110 	 Time Taken: 235 sec
learning rate 0.0001010 -> 0.0000990
(epoch: 60, iters: 100, time: 0.807, data: 0.162) D_A: 0.167 G_A: 0.548 cycle_A: 1.207 idt_A: 0.592 D_B: 0.109 G_B: 0.451 cycle_B: 1.246 idt_B: 0.615 
(epoch: 60, iters: 200, time: 0.575, data: 0.003) D_A: 0.193 G_A: 0.204 cycle_A: 1.304 idt_A: 0.455 D_B: 0.172 G_B: 0.288 cycle_B: 1.048 idt_B: 0.613 
(epoch: 60, iters: 300, time: 0.577, data: 0.003) D_A: 0.123 G_A: 0.429 cycle_A: 1.545 idt_A: 0.526 D_B: 0.097 G_B: 0.507 cycle_B: 1.026 idt_B: 0.715 
(epoch: 60, iters: 400, time: 0.574, data: 0.004) D_A: 0.213 G_A: 0.258 cycle_A: 1.244 idt_A: 0.403 D_B: 0.097 G_B: 0.380 cycle_B: 0.998 idt_B: 0.703 
(epoch: 60, iters: 500, time: 0.744, data: 0.003) D_A: 0.312 G_A: 0.755 cycle_A: 1.407 idt_A: 0.511 D_B: 0.148 G_B: 0.370 cycle_B: 1.148 idt_B: 0.781 
saving the latest model (epoch 60, total_iters 30000)
saving the model at the end of epoch 60, iters 30000
End of epoch 60 / 110 	 Time Taken: 237 sec
learning rate 0.0000990 -> 0.0000970
(epoch: 61, iters: 100, time: 0.573, data: 0.149) D_A: 0.101 G_A: 0.523 cycle_A: 1.104 idt_A: 0.395 D_B: 0.219 G_B: 0.503 cycle_B: 0.812 idt_B: 0.583 
(epoch: 61, iters: 200, time: 0.573, data: 0.003) D_A: 0.137 G_A: 0.648 cycle_A: 1.713 idt_A: 0.483 D_B: 0.154 G_B: 0.391 cycle_B: 1.064 idt_B: 0.738 
(epoch: 61, iters: 300, time: 0.575, data: 0.003) D_A: 0.162 G_A: 0.470 cycle_A: 1.292 idt_A: 0.484 D_B: 0.128 G_B: 0.233 cycle_B: 1.003 idt_B: 0.649 
(epoch: 61, iters: 400, time: 0.823, data: 0.003) D_A: 0.248 G_A: 0.193 cycle_A: 1.374 idt_A: 0.731 D_B: 0.185 G_B: 0.352 cycle_B: 1.226 idt_B: 0.549 
(epoch: 61, iters: 500, time: 0.577, data: 0.003) D_A: 0.242 G_A: 0.553 cycle_A: 1.062 idt_A: 0.480 D_B: 0.113 G_B: 0.367 cycle_B: 1.078 idt_B: 0.595 
End of epoch 61 / 110 	 Time Taken: 235 sec
learning rate 0.0000970 -> 0.0000950
(epoch: 62, iters: 100, time: 0.576, data: 0.148) D_A: 0.157 G_A: 0.495 cycle_A: 1.427 idt_A: 0.577 D_B: 0.146 G_B: 0.396 cycle_B: 1.148 idt_B: 0.717 
(epoch: 62, iters: 200, time: 0.574, data: 0.003) D_A: 0.121 G_A: 0.332 cycle_A: 1.116 idt_A: 0.508 D_B: 0.211 G_B: 0.534 cycle_B: 1.072 idt_B: 0.674 
(epoch: 62, iters: 300, time: 0.824, data: 0.003) D_A: 0.249 G_A: 0.401 cycle_A: 1.172 idt_A: 0.531 D_B: 0.132 G_B: 0.332 cycle_B: 1.132 idt_B: 0.587 
(epoch: 62, iters: 400, time: 0.574, data: 0.003) D_A: 0.236 G_A: 0.462 cycle_A: 1.413 idt_A: 0.827 D_B: 0.140 G_B: 0.334 cycle_B: 1.413 idt_B: 0.660 
(epoch: 62, iters: 500, time: 0.575, data: 0.003) D_A: 0.218 G_A: 0.228 cycle_A: 1.611 idt_A: 0.545 D_B: 0.118 G_B: 0.291 cycle_B: 1.159 idt_B: 0.744 
End of epoch 62 / 110 	 Time Taken: 235 sec
learning rate 0.0000950 -> 0.0000931
(epoch: 63, iters: 100, time: 0.574, data: 0.166) D_A: 0.121 G_A: 0.380 cycle_A: 0.977 idt_A: 0.397 D_B: 0.200 G_B: 0.512 cycle_B: 0.929 idt_B: 0.503 
(epoch: 63, iters: 200, time: 0.840, data: 0.003) D_A: 0.196 G_A: 0.220 cycle_A: 1.562 idt_A: 0.362 D_B: 0.267 G_B: 0.773 cycle_B: 0.853 idt_B: 0.890 
(epoch: 63, iters: 300, time: 0.575, data: 0.003) D_A: 0.100 G_A: 0.760 cycle_A: 1.164 idt_A: 0.561 D_B: 0.211 G_B: 0.803 cycle_B: 1.215 idt_B: 0.623 
(epoch: 63, iters: 400, time: 0.575, data: 0.003) D_A: 0.178 G_A: 0.355 cycle_A: 0.941 idt_A: 0.496 D_B: 0.297 G_B: 0.082 cycle_B: 1.037 idt_B: 0.478 
(epoch: 63, iters: 500, time: 0.575, data: 0.003) D_A: 0.193 G_A: 0.343 cycle_A: 1.169 idt_A: 0.415 D_B: 0.156 G_B: 0.399 cycle_B: 0.932 idt_B: 0.588 
End of epoch 63 / 110 	 Time Taken: 235 sec
learning rate 0.0000931 -> 0.0000911
(epoch: 64, iters: 100, time: 0.832, data: 0.151) D_A: 0.115 G_A: 0.518 cycle_A: 1.114 idt_A: 0.419 D_B: 0.124 G_B: 0.332 cycle_B: 0.927 idt_B: 0.583 
(epoch: 64, iters: 200, time: 0.574, data: 0.003) D_A: 0.211 G_A: 0.300 cycle_A: 1.276 idt_A: 0.481 D_B: 0.137 G_B: 0.628 cycle_B: 1.212 idt_B: 0.545 
(epoch: 64, iters: 300, time: 0.574, data: 0.003) D_A: 0.167 G_A: 0.383 cycle_A: 1.271 idt_A: 0.533 D_B: 0.185 G_B: 0.252 cycle_B: 1.033 idt_B: 0.493 
(epoch: 64, iters: 400, time: 0.574, data: 0.003) D_A: 0.235 G_A: 0.197 cycle_A: 1.199 idt_A: 0.625 D_B: 0.303 G_B: 0.676 cycle_B: 1.282 idt_B: 0.569 
(epoch: 64, iters: 500, time: 0.828, data: 0.003) D_A: 0.135 G_A: 0.418 cycle_A: 1.081 idt_A: 0.577 D_B: 0.161 G_B: 0.540 cycle_B: 1.062 idt_B: 0.921 
End of epoch 64 / 110 	 Time Taken: 236 sec
learning rate 0.0000911 -> 0.0000891
(epoch: 65, iters: 100, time: 0.574, data: 0.153) D_A: 0.176 G_A: 0.659 cycle_A: 1.202 idt_A: 0.527 D_B: 0.143 G_B: 0.460 cycle_B: 1.201 idt_B: 0.621 
(epoch: 65, iters: 200, time: 0.575, data: 0.003) D_A: 0.101 G_A: 0.618 cycle_A: 1.074 idt_A: 0.408 D_B: 0.107 G_B: 0.518 cycle_B: 0.938 idt_B: 0.608 
(epoch: 65, iters: 300, time: 0.573, data: 0.003) D_A: 0.280 G_A: 0.180 cycle_A: 1.446 idt_A: 0.455 D_B: 0.184 G_B: 0.282 cycle_B: 0.981 idt_B: 0.649 
(epoch: 65, iters: 400, time: 0.830, data: 0.003) D_A: 0.170 G_A: 0.321 cycle_A: 1.038 idt_A: 0.700 D_B: 0.158 G_B: 0.419 cycle_B: 1.072 idt_B: 0.620 
(epoch: 65, iters: 500, time: 0.574, data: 0.003) D_A: 0.231 G_A: 0.295 cycle_A: 1.294 idt_A: 0.574 D_B: 0.209 G_B: 0.606 cycle_B: 1.241 idt_B: 0.697 
saving the model at the end of epoch 65, iters 32500
End of epoch 65 / 110 	 Time Taken: 235 sec
learning rate 0.0000891 -> 0.0000871
(epoch: 66, iters: 100, time: 0.576, data: 0.174) D_A: 0.198 G_A: 0.405 cycle_A: 1.079 idt_A: 0.478 D_B: 0.175 G_B: 0.350 cycle_B: 1.127 idt_B: 0.547 
(epoch: 66, iters: 200, time: 0.574, data: 0.003) D_A: 0.148 G_A: 0.418 cycle_A: 1.038 idt_A: 0.346 D_B: 0.108 G_B: 0.221 cycle_B: 0.938 idt_B: 0.467 
(epoch: 66, iters: 300, time: 0.826, data: 0.004) D_A: 0.211 G_A: 0.321 cycle_A: 0.936 idt_A: 0.603 D_B: 0.203 G_B: 0.338 cycle_B: 1.071 idt_B: 0.551 
(epoch: 66, iters: 400, time: 0.573, data: 0.003) D_A: 0.219 G_A: 0.628 cycle_A: 1.583 idt_A: 0.508 D_B: 0.230 G_B: 0.367 cycle_B: 0.915 idt_B: 0.798 
(epoch: 66, iters: 500, time: 0.574, data: 0.003) D_A: 0.336 G_A: 0.369 cycle_A: 1.283 idt_A: 0.523 D_B: 0.202 G_B: 0.262 cycle_B: 0.986 idt_B: 0.659 
End of epoch 66 / 110 	 Time Taken: 234 sec
learning rate 0.0000871 -> 0.0000851
(epoch: 67, iters: 100, time: 0.575, data: 0.141) D_A: 0.125 G_A: 0.513 cycle_A: 1.202 idt_A: 0.424 D_B: 0.197 G_B: 0.340 cycle_B: 0.973 idt_B: 0.561 
(epoch: 67, iters: 200, time: 0.865, data: 0.003) D_A: 0.153 G_A: 0.370 cycle_A: 1.146 idt_A: 0.454 D_B: 0.131 G_B: 0.350 cycle_B: 0.992 idt_B: 0.625 
(epoch: 67, iters: 300, time: 0.576, data: 0.003) D_A: 0.225 G_A: 0.245 cycle_A: 0.937 idt_A: 0.465 D_B: 0.167 G_B: 0.361 cycle_B: 1.193 idt_B: 0.469 
(epoch: 67, iters: 400, time: 0.574, data: 0.003) D_A: 0.178 G_A: 0.559 cycle_A: 0.856 idt_A: 0.466 D_B: 0.151 G_B: 0.219 cycle_B: 0.904 idt_B: 0.486 
(epoch: 67, iters: 500, time: 0.576, data: 0.003) D_A: 0.240 G_A: 0.216 cycle_A: 1.006 idt_A: 0.549 D_B: 0.210 G_B: 0.397 cycle_B: 1.032 idt_B: 0.588 
End of epoch 67 / 110 	 Time Taken: 235 sec
learning rate 0.0000851 -> 0.0000832
(epoch: 68, iters: 100, time: 0.847, data: 0.139) D_A: 0.185 G_A: 0.429 cycle_A: 1.237 idt_A: 0.473 D_B: 0.219 G_B: 0.203 cycle_B: 1.007 idt_B: 0.651 
(epoch: 68, iters: 200, time: 0.576, data: 0.003) D_A: 0.187 G_A: 0.275 cycle_A: 1.444 idt_A: 0.470 D_B: 0.259 G_B: 0.184 cycle_B: 1.018 idt_B: 0.657 
(epoch: 68, iters: 300, time: 0.577, data: 0.003) D_A: 0.239 G_A: 0.267 cycle_A: 1.263 idt_A: 0.461 D_B: 0.200 G_B: 0.382 cycle_B: 1.096 idt_B: 0.597 
(epoch: 68, iters: 400, time: 0.576, data: 0.003) D_A: 0.134 G_A: 0.480 cycle_A: 1.335 idt_A: 0.397 D_B: 0.145 G_B: 0.476 cycle_B: 0.876 idt_B: 0.660 
(epoch: 68, iters: 500, time: 0.857, data: 0.003) D_A: 0.211 G_A: 0.372 cycle_A: 1.242 idt_A: 0.589 D_B: 0.154 G_B: 0.399 cycle_B: 1.219 idt_B: 0.561 
End of epoch 68 / 110 	 Time Taken: 236 sec
learning rate 0.0000832 -> 0.0000812
(epoch: 69, iters: 100, time: 0.573, data: 0.188) D_A: 0.200 G_A: 0.516 cycle_A: 0.907 idt_A: 0.464 D_B: 0.217 G_B: 0.273 cycle_B: 0.939 idt_B: 0.491 
(epoch: 69, iters: 200, time: 0.577, data: 0.003) D_A: 0.218 G_A: 0.448 cycle_A: 1.087 idt_A: 0.671 D_B: 0.236 G_B: 0.465 cycle_B: 1.098 idt_B: 0.517 
(epoch: 69, iters: 300, time: 0.572, data: 0.003) D_A: 0.289 G_A: 0.573 cycle_A: 1.287 idt_A: 0.475 D_B: 0.213 G_B: 0.336 cycle_B: 1.085 idt_B: 0.605 
(epoch: 69, iters: 400, time: 0.828, data: 0.004) D_A: 0.263 G_A: 0.295 cycle_A: 1.169 idt_A: 0.558 D_B: 0.289 G_B: 0.278 cycle_B: 1.134 idt_B: 0.580 
(epoch: 69, iters: 500, time: 0.574, data: 0.002) D_A: 0.162 G_A: 0.562 cycle_A: 1.155 idt_A: 0.476 D_B: 0.286 G_B: 0.142 cycle_B: 0.970 idt_B: 0.661 
End of epoch 69 / 110 	 Time Taken: 235 sec
learning rate 0.0000812 -> 0.0000792
(epoch: 70, iters: 100, time: 0.575, data: 0.131) D_A: 0.152 G_A: 0.331 cycle_A: 1.038 idt_A: 0.355 D_B: 0.165 G_B: 0.694 cycle_B: 0.807 idt_B: 0.521 
(epoch: 70, iters: 200, time: 0.574, data: 0.003) D_A: 0.142 G_A: 0.331 cycle_A: 1.224 idt_A: 0.442 D_B: 0.172 G_B: 0.568 cycle_B: 1.025 idt_B: 0.651 
(epoch: 70, iters: 300, time: 0.857, data: 0.003) D_A: 0.232 G_A: 0.371 cycle_A: 1.115 idt_A: 0.445 D_B: 0.149 G_B: 0.436 cycle_B: 0.867 idt_B: 0.532 
(epoch: 70, iters: 400, time: 0.575, data: 0.003) D_A: 0.268 G_A: 0.211 cycle_A: 1.221 idt_A: 0.573 D_B: 0.223 G_B: 0.526 cycle_B: 1.189 idt_B: 0.543 
(epoch: 70, iters: 500, time: 0.577, data: 0.003) D_A: 0.109 G_A: 0.495 cycle_A: 1.313 idt_A: 0.485 D_B: 0.128 G_B: 0.401 cycle_B: 1.049 idt_B: 0.670 
saving the latest model (epoch 70, total_iters 35000)
saving the model at the end of epoch 70, iters 35000
End of epoch 70 / 110 	 Time Taken: 236 sec
learning rate 0.0000792 -> 0.0000772
(epoch: 71, iters: 100, time: 0.575, data: 0.166) D_A: 0.172 G_A: 0.289 cycle_A: 1.146 idt_A: 0.446 D_B: 0.147 G_B: 0.480 cycle_B: 0.923 idt_B: 0.633 
(epoch: 71, iters: 200, time: 0.836, data: 0.003) D_A: 0.096 G_A: 0.255 cycle_A: 1.214 idt_A: 0.581 D_B: 0.152 G_B: 0.645 cycle_B: 1.103 idt_B: 0.790 
(epoch: 71, iters: 300, time: 0.575, data: 0.003) D_A: 0.144 G_A: 0.684 cycle_A: 1.153 idt_A: 0.495 D_B: 0.106 G_B: 0.384 cycle_B: 1.125 idt_B: 0.507 
(epoch: 71, iters: 400, time: 0.575, data: 0.003) D_A: 0.163 G_A: 0.299 cycle_A: 1.186 idt_A: 0.357 D_B: 0.110 G_B: 0.411 cycle_B: 0.989 idt_B: 0.639 
(epoch: 71, iters: 500, time: 0.575, data: 0.003) D_A: 0.169 G_A: 0.336 cycle_A: 1.283 idt_A: 0.445 D_B: 0.145 G_B: 0.564 cycle_B: 0.939 idt_B: 0.548 
End of epoch 71 / 110 	 Time Taken: 235 sec
learning rate 0.0000772 -> 0.0000752
(epoch: 72, iters: 100, time: 0.880, data: 0.167) D_A: 0.119 G_A: 0.355 cycle_A: 1.133 idt_A: 0.301 D_B: 0.169 G_B: 0.565 cycle_B: 0.722 idt_B: 0.569 
(epoch: 72, iters: 200, time: 0.574, data: 0.003) D_A: 0.145 G_A: 0.449 cycle_A: 0.909 idt_A: 0.642 D_B: 0.182 G_B: 0.351 cycle_B: 1.399 idt_B: 0.405 
(epoch: 72, iters: 300, time: 0.574, data: 0.003) D_A: 0.150 G_A: 0.509 cycle_A: 0.904 idt_A: 0.504 D_B: 0.170 G_B: 0.275 cycle_B: 1.042 idt_B: 0.509 
(epoch: 72, iters: 400, time: 0.578, data: 0.003) D_A: 0.217 G_A: 0.341 cycle_A: 1.375 idt_A: 0.446 D_B: 0.133 G_B: 0.363 cycle_B: 1.091 idt_B: 0.703 
(epoch: 72, iters: 500, time: 0.844, data: 0.003) D_A: 0.115 G_A: 0.542 cycle_A: 1.002 idt_A: 0.403 D_B: 0.162 G_B: 0.613 cycle_B: 0.933 idt_B: 0.523 
End of epoch 72 / 110 	 Time Taken: 236 sec
learning rate 0.0000752 -> 0.0000733
(epoch: 73, iters: 100, time: 0.575, data: 0.159) D_A: 0.241 G_A: 0.270 cycle_A: 1.001 idt_A: 0.349 D_B: 0.170 G_B: 0.279 cycle_B: 0.788 idt_B: 0.556 
(epoch: 73, iters: 200, time: 0.575, data: 0.003) D_A: 0.221 G_A: 0.466 cycle_A: 1.422 idt_A: 0.559 D_B: 0.111 G_B: 0.515 cycle_B: 1.234 idt_B: 0.647 
(epoch: 73, iters: 300, time: 0.576, data: 0.003) D_A: 0.133 G_A: 0.234 cycle_A: 1.119 idt_A: 0.444 D_B: 0.200 G_B: 0.302 cycle_B: 0.956 idt_B: 0.568 
(epoch: 73, iters: 400, time: 0.846, data: 0.003) D_A: 0.210 G_A: 0.523 cycle_A: 1.060 idt_A: 0.566 D_B: 0.264 G_B: 0.560 cycle_B: 1.124 idt_B: 0.497 
(epoch: 73, iters: 500, time: 0.575, data: 0.003) D_A: 0.258 G_A: 0.303 cycle_A: 0.973 idt_A: 0.403 D_B: 0.231 G_B: 0.270 cycle_B: 0.784 idt_B: 0.453 
End of epoch 73 / 110 	 Time Taken: 235 sec
learning rate 0.0000733 -> 0.0000713
(epoch: 74, iters: 100, time: 0.573, data: 0.148) D_A: 0.158 G_A: 0.306 cycle_A: 1.144 idt_A: 0.328 D_B: 0.179 G_B: 0.503 cycle_B: 1.047 idt_B: 0.640 
(epoch: 74, iters: 200, time: 0.574, data: 0.003) D_A: 0.181 G_A: 0.186 cycle_A: 1.355 idt_A: 0.519 D_B: 0.151 G_B: 0.428 cycle_B: 1.052 idt_B: 0.715 
(epoch: 74, iters: 300, time: 0.851, data: 0.003) D_A: 0.228 G_A: 0.416 cycle_A: 1.182 idt_A: 0.585 D_B: 0.139 G_B: 0.334 cycle_B: 1.241 idt_B: 0.714 
(epoch: 74, iters: 400, time: 0.573, data: 0.003) D_A: 0.159 G_A: 0.258 cycle_A: 1.035 idt_A: 0.461 D_B: 0.261 G_B: 0.186 cycle_B: 0.876 idt_B: 0.524 
(epoch: 74, iters: 500, time: 0.575, data: 0.003) D_A: 0.103 G_A: 0.638 cycle_A: 0.978 idt_A: 0.432 D_B: 0.086 G_B: 0.496 cycle_B: 0.969 idt_B: 0.482 
End of epoch 74 / 110 	 Time Taken: 235 sec
learning rate 0.0000713 -> 0.0000693
(epoch: 75, iters: 100, time: 0.576, data: 0.176) D_A: 0.172 G_A: 0.632 cycle_A: 1.067 idt_A: 0.459 D_B: 0.140 G_B: 0.416 cycle_B: 1.001 idt_B: 0.550 
(epoch: 75, iters: 200, time: 0.855, data: 0.003) D_A: 0.285 G_A: 0.346 cycle_A: 0.930 idt_A: 0.444 D_B: 0.155 G_B: 0.329 cycle_B: 0.965 idt_B: 0.549 
(epoch: 75, iters: 300, time: 0.575, data: 0.003) D_A: 0.137 G_A: 0.265 cycle_A: 0.990 idt_A: 0.515 D_B: 0.183 G_B: 0.274 cycle_B: 1.206 idt_B: 0.498 
(epoch: 75, iters: 400, time: 0.575, data: 0.003) D_A: 0.161 G_A: 0.675 cycle_A: 1.130 idt_A: 0.502 D_B: 0.191 G_B: 0.232 cycle_B: 1.094 idt_B: 0.492 
(epoch: 75, iters: 500, time: 0.574, data: 0.003) D_A: 0.172 G_A: 0.435 cycle_A: 1.008 idt_A: 0.525 D_B: 0.206 G_B: 0.320 cycle_B: 1.136 idt_B: 0.576 
saving the model at the end of epoch 75, iters 37500
End of epoch 75 / 110 	 Time Taken: 236 sec
learning rate 0.0000693 -> 0.0000673
(epoch: 76, iters: 100, time: 0.851, data: 0.166) D_A: 0.150 G_A: 0.384 cycle_A: 1.204 idt_A: 0.427 D_B: 0.285 G_B: 0.704 cycle_B: 1.147 idt_B: 0.600 
(epoch: 76, iters: 200, time: 0.574, data: 0.003) D_A: 0.167 G_A: 0.489 cycle_A: 1.097 idt_A: 0.409 D_B: 0.119 G_B: 0.497 cycle_B: 0.928 idt_B: 0.457 
(epoch: 76, iters: 300, time: 0.574, data: 0.003) D_A: 0.175 G_A: 0.673 cycle_A: 1.667 idt_A: 0.415 D_B: 0.135 G_B: 0.470 cycle_B: 0.839 idt_B: 0.892 
(epoch: 76, iters: 400, time: 0.575, data: 0.003) D_A: 0.251 G_A: 0.459 cycle_A: 0.892 idt_A: 0.441 D_B: 0.195 G_B: 0.518 cycle_B: 1.022 idt_B: 0.487 
(epoch: 76, iters: 500, time: 0.858, data: 0.003) D_A: 0.197 G_A: 0.393 cycle_A: 1.431 idt_A: 0.481 D_B: 0.137 G_B: 0.534 cycle_B: 1.026 idt_B: 0.827 
End of epoch 76 / 110 	 Time Taken: 236 sec
learning rate 0.0000673 -> 0.0000653
(epoch: 77, iters: 100, time: 0.574, data: 0.169) D_A: 0.154 G_A: 0.436 cycle_A: 1.323 idt_A: 0.464 D_B: 0.110 G_B: 0.527 cycle_B: 1.017 idt_B: 0.602 
(epoch: 77, iters: 200, time: 0.574, data: 0.003) D_A: 0.190 G_A: 0.427 cycle_A: 1.409 idt_A: 0.493 D_B: 0.074 G_B: 0.551 cycle_B: 1.012 idt_B: 0.728 
(epoch: 77, iters: 300, time: 0.574, data: 0.003) D_A: 0.115 G_A: 0.421 cycle_A: 1.126 idt_A: 0.478 D_B: 0.160 G_B: 0.537 cycle_B: 1.110 idt_B: 0.587 
(epoch: 77, iters: 400, time: 0.854, data: 0.003) D_A: 0.123 G_A: 0.326 cycle_A: 1.210 idt_A: 0.312 D_B: 0.142 G_B: 0.243 cycle_B: 0.669 idt_B: 0.611 
(epoch: 77, iters: 500, time: 0.575, data: 0.003) D_A: 0.095 G_A: 0.537 cycle_A: 0.939 idt_A: 0.383 D_B: 0.163 G_B: 0.537 cycle_B: 0.894 idt_B: 0.447 
End of epoch 77 / 110 	 Time Taken: 235 sec
learning rate 0.0000653 -> 0.0000634
(epoch: 78, iters: 100, time: 0.576, data: 0.163) D_A: 0.200 G_A: 0.598 cycle_A: 1.118 idt_A: 0.555 D_B: 0.101 G_B: 0.286 cycle_B: 1.112 idt_B: 0.544 
(epoch: 78, iters: 200, time: 0.576, data: 0.003) D_A: 0.164 G_A: 0.559 cycle_A: 1.073 idt_A: 0.505 D_B: 0.159 G_B: 0.369 cycle_B: 0.879 idt_B: 0.644 
(epoch: 78, iters: 300, time: 0.860, data: 0.003) D_A: 0.172 G_A: 0.361 cycle_A: 0.918 idt_A: 0.444 D_B: 0.166 G_B: 0.621 cycle_B: 0.907 idt_B: 0.496 
(epoch: 78, iters: 400, time: 0.574, data: 0.003) D_A: 0.179 G_A: 0.410 cycle_A: 0.858 idt_A: 0.402 D_B: 0.128 G_B: 0.248 cycle_B: 0.791 idt_B: 0.591 
(epoch: 78, iters: 500, time: 0.575, data: 0.003) D_A: 0.120 G_A: 0.518 cycle_A: 1.080 idt_A: 0.356 D_B: 0.151 G_B: 0.306 cycle_B: 1.010 idt_B: 0.532 
End of epoch 78 / 110 	 Time Taken: 235 sec
learning rate 0.0000634 -> 0.0000614
(epoch: 79, iters: 100, time: 0.575, data: 0.164) D_A: 0.203 G_A: 0.538 cycle_A: 1.185 idt_A: 0.337 D_B: 0.121 G_B: 0.533 cycle_B: 0.766 idt_B: 0.638 
(epoch: 79, iters: 200, time: 0.865, data: 0.003) D_A: 0.175 G_A: 0.340 cycle_A: 0.931 idt_A: 0.492 D_B: 0.150 G_B: 0.597 cycle_B: 1.103 idt_B: 0.549 
(epoch: 79, iters: 300, time: 0.575, data: 0.003) D_A: 0.227 G_A: 0.247 cycle_A: 0.926 idt_A: 0.609 D_B: 0.142 G_B: 0.311 cycle_B: 0.958 idt_B: 0.473 
(epoch: 79, iters: 400, time: 0.574, data: 0.003) D_A: 0.195 G_A: 0.288 cycle_A: 1.108 idt_A: 0.454 D_B: 0.256 G_B: 0.615 cycle_B: 0.925 idt_B: 0.637 
(epoch: 79, iters: 500, time: 0.576, data: 0.003) D_A: 0.195 G_A: 0.650 cycle_A: 1.067 idt_A: 0.385 D_B: 0.091 G_B: 0.497 cycle_B: 0.788 idt_B: 0.657 
End of epoch 79 / 110 	 Time Taken: 235 sec
learning rate 0.0000614 -> 0.0000594
(epoch: 80, iters: 100, time: 0.844, data: 0.158) D_A: 0.167 G_A: 0.554 cycle_A: 1.196 idt_A: 0.491 D_B: 0.163 G_B: 0.440 cycle_B: 0.996 idt_B: 0.543 
(epoch: 80, iters: 200, time: 0.577, data: 0.003) D_A: 0.164 G_A: 0.498 cycle_A: 0.982 idt_A: 0.380 D_B: 0.133 G_B: 0.619 cycle_B: 0.822 idt_B: 0.587 
(epoch: 80, iters: 300, time: 0.573, data: 0.003) D_A: 0.157 G_A: 0.390 cycle_A: 1.082 idt_A: 0.528 D_B: 0.153 G_B: 0.181 cycle_B: 1.020 idt_B: 0.476 
(epoch: 80, iters: 400, time: 0.575, data: 0.003) D_A: 0.160 G_A: 0.510 cycle_A: 1.155 idt_A: 0.412 D_B: 0.187 G_B: 0.323 cycle_B: 0.855 idt_B: 0.505 
(epoch: 80, iters: 500, time: 0.861, data: 0.003) D_A: 0.152 G_A: 0.321 cycle_A: 0.897 idt_A: 0.368 D_B: 0.157 G_B: 0.575 cycle_B: 0.846 idt_B: 0.543 
saving the latest model (epoch 80, total_iters 40000)
saving the model at the end of epoch 80, iters 40000
End of epoch 80 / 110 	 Time Taken: 237 sec
learning rate 0.0000594 -> 0.0000574
(epoch: 81, iters: 100, time: 0.572, data: 0.162) D_A: 0.235 G_A: 0.492 cycle_A: 0.893 idt_A: 0.509 D_B: 0.127 G_B: 0.185 cycle_B: 1.061 idt_B: 0.497 
(epoch: 81, iters: 200, time: 0.574, data: 0.003) D_A: 0.232 G_A: 0.466 cycle_A: 0.837 idt_A: 0.524 D_B: 0.210 G_B: 0.197 cycle_B: 1.073 idt_B: 0.437 
(epoch: 81, iters: 300, time: 0.573, data: 0.003) D_A: 0.156 G_A: 0.324 cycle_A: 1.057 idt_A: 0.391 D_B: 0.182 G_B: 0.354 cycle_B: 0.822 idt_B: 0.550 
(epoch: 81, iters: 400, time: 0.879, data: 0.003) D_A: 0.109 G_A: 0.400 cycle_A: 1.026 idt_A: 0.489 D_B: 0.195 G_B: 0.516 cycle_B: 0.831 idt_B: 0.907 
(epoch: 81, iters: 500, time: 0.573, data: 0.003) D_A: 0.162 G_A: 0.255 cycle_A: 1.160 idt_A: 0.434 D_B: 0.223 G_B: 0.232 cycle_B: 0.987 idt_B: 0.500 
End of epoch 81 / 110 	 Time Taken: 234 sec
learning rate 0.0000574 -> 0.0000554
(epoch: 82, iters: 100, time: 0.574, data: 0.160) D_A: 0.239 G_A: 0.555 cycle_A: 0.806 idt_A: 0.408 D_B: 0.162 G_B: 0.360 cycle_B: 0.946 idt_B: 0.428 
(epoch: 82, iters: 200, time: 0.574, data: 0.003) D_A: 0.218 G_A: 0.281 cycle_A: 1.533 idt_A: 0.399 D_B: 0.096 G_B: 0.264 cycle_B: 0.788 idt_B: 0.728 
(epoch: 82, iters: 300, time: 0.860, data: 0.004) D_A: 0.163 G_A: 0.373 cycle_A: 0.908 idt_A: 0.422 D_B: 0.219 G_B: 0.396 cycle_B: 0.815 idt_B: 0.497 
(epoch: 82, iters: 400, time: 0.575, data: 0.003) D_A: 0.209 G_A: 0.307 cycle_A: 0.993 idt_A: 0.487 D_B: 0.147 G_B: 0.191 cycle_B: 1.011 idt_B: 0.399 
(epoch: 82, iters: 500, time: 0.574, data: 0.003) D_A: 0.170 G_A: 0.560 cycle_A: 0.925 idt_A: 0.392 D_B: 0.228 G_B: 0.640 cycle_B: 0.777 idt_B: 0.533 
End of epoch 82 / 110 	 Time Taken: 235 sec
learning rate 0.0000554 -> 0.0000535
(epoch: 83, iters: 100, time: 0.574, data: 0.156) D_A: 0.105 G_A: 0.285 cycle_A: 1.056 idt_A: 0.302 D_B: 0.223 G_B: 0.318 cycle_B: 0.692 idt_B: 0.514 
(epoch: 83, iters: 200, time: 0.855, data: 0.003) D_A: 0.182 G_A: 0.487 cycle_A: 1.117 idt_A: 0.477 D_B: 0.208 G_B: 0.363 cycle_B: 0.930 idt_B: 0.584 
(epoch: 83, iters: 300, time: 0.576, data: 0.003) D_A: 0.121 G_A: 0.787 cycle_A: 1.169 idt_A: 0.494 D_B: 0.112 G_B: 0.324 cycle_B: 1.155 idt_B: 0.712 
(epoch: 83, iters: 400, time: 0.573, data: 0.003) D_A: 0.261 G_A: 0.322 cycle_A: 1.025 idt_A: 0.468 D_B: 0.143 G_B: 0.252 cycle_B: 0.881 idt_B: 0.537 
(epoch: 83, iters: 500, time: 0.572, data: 0.003) D_A: 0.117 G_A: 0.508 cycle_A: 1.009 idt_A: 0.442 D_B: 0.163 G_B: 0.483 cycle_B: 1.005 idt_B: 0.365 
End of epoch 83 / 110 	 Time Taken: 235 sec
learning rate 0.0000535 -> 0.0000515
(epoch: 84, iters: 100, time: 0.872, data: 0.186) D_A: 0.201 G_A: 0.393 cycle_A: 1.057 idt_A: 0.463 D_B: 0.186 G_B: 0.286 cycle_B: 0.987 idt_B: 0.589 
(epoch: 84, iters: 200, time: 0.573, data: 0.003) D_A: 0.207 G_A: 0.288 cycle_A: 1.232 idt_A: 0.398 D_B: 0.158 G_B: 0.266 cycle_B: 0.858 idt_B: 0.563 
(epoch: 84, iters: 300, time: 0.576, data: 0.003) D_A: 0.213 G_A: 0.451 cycle_A: 1.059 idt_A: 0.431 D_B: 0.153 G_B: 0.386 cycle_B: 0.901 idt_B: 0.562 
(epoch: 84, iters: 400, time: 0.570, data: 0.003) D_A: 0.161 G_A: 0.422 cycle_A: 0.889 idt_A: 0.476 D_B: 0.182 G_B: 0.468 cycle_B: 0.888 idt_B: 0.548 
(epoch: 84, iters: 500, time: 0.887, data: 0.003) D_A: 0.115 G_A: 0.738 cycle_A: 1.157 idt_A: 0.443 D_B: 0.072 G_B: 0.657 cycle_B: 0.960 idt_B: 0.651 
End of epoch 84 / 110 	 Time Taken: 236 sec
learning rate 0.0000515 -> 0.0000495
(epoch: 85, iters: 100, time: 0.571, data: 0.138) D_A: 0.186 G_A: 0.390 cycle_A: 1.000 idt_A: 0.431 D_B: 0.211 G_B: 0.265 cycle_B: 0.935 idt_B: 0.568 
(epoch: 85, iters: 200, time: 0.579, data: 0.003) D_A: 0.138 G_A: 0.492 cycle_A: 1.029 idt_A: 0.464 D_B: 0.119 G_B: 0.538 cycle_B: 0.798 idt_B: 0.610 
(epoch: 85, iters: 300, time: 0.570, data: 0.003) D_A: 0.183 G_A: 0.485 cycle_A: 1.042 idt_A: 0.417 D_B: 0.220 G_B: 0.341 cycle_B: 0.934 idt_B: 0.500 
(epoch: 85, iters: 400, time: 0.856, data: 0.003) D_A: 0.189 G_A: 0.546 cycle_A: 0.995 idt_A: 0.505 D_B: 0.184 G_B: 0.381 cycle_B: 1.074 idt_B: 0.503 
(epoch: 85, iters: 500, time: 0.574, data: 0.003) D_A: 0.209 G_A: 0.487 cycle_A: 0.956 idt_A: 0.442 D_B: 0.114 G_B: 0.435 cycle_B: 0.928 idt_B: 0.505 
saving the model at the end of epoch 85, iters 42500
End of epoch 85 / 110 	 Time Taken: 235 sec
learning rate 0.0000495 -> 0.0000475
(epoch: 86, iters: 100, time: 0.572, data: 0.159) D_A: 0.172 G_A: 0.701 cycle_A: 1.146 idt_A: 0.385 D_B: 0.076 G_B: 0.326 cycle_B: 0.891 idt_B: 0.618 
(epoch: 86, iters: 200, time: 0.576, data: 0.003) D_A: 0.088 G_A: 0.535 cycle_A: 0.929 idt_A: 0.373 D_B: 0.112 G_B: 0.496 cycle_B: 0.770 idt_B: 0.463 
(epoch: 86, iters: 300, time: 0.865, data: 0.003) D_A: 0.091 G_A: 0.515 cycle_A: 0.989 idt_A: 0.300 D_B: 0.122 G_B: 0.542 cycle_B: 0.713 idt_B: 0.526 
(epoch: 86, iters: 400, time: 0.577, data: 0.003) D_A: 0.148 G_A: 0.579 cycle_A: 1.117 idt_A: 0.431 D_B: 0.133 G_B: 0.443 cycle_B: 0.988 idt_B: 0.488 
(epoch: 86, iters: 500, time: 0.571, data: 0.003) D_A: 0.114 G_A: 0.380 cycle_A: 0.879 idt_A: 0.343 D_B: 0.142 G_B: 0.607 cycle_B: 0.706 idt_B: 0.568 
End of epoch 86 / 110 	 Time Taken: 234 sec
learning rate 0.0000475 -> 0.0000455
(epoch: 87, iters: 100, time: 0.576, data: 0.145) D_A: 0.126 G_A: 0.402 cycle_A: 0.926 idt_A: 0.489 D_B: 0.109 G_B: 0.559 cycle_B: 1.001 idt_B: 0.494 
(epoch: 87, iters: 200, time: 0.878, data: 0.003) D_A: 0.128 G_A: 0.344 cycle_A: 1.197 idt_A: 0.477 D_B: 0.129 G_B: 0.332 cycle_B: 0.966 idt_B: 0.482 
(epoch: 87, iters: 300, time: 0.573, data: 0.003) D_A: 0.096 G_A: 0.514 cycle_A: 1.126 idt_A: 0.348 D_B: 0.151 G_B: 0.427 cycle_B: 0.860 idt_B: 0.538 
(epoch: 87, iters: 400, time: 0.573, data: 0.003) D_A: 0.106 G_A: 0.654 cycle_A: 1.381 idt_A: 0.463 D_B: 0.101 G_B: 0.443 cycle_B: 0.959 idt_B: 0.664 
(epoch: 87, iters: 500, time: 0.573, data: 0.003) D_A: 0.075 G_A: 0.498 cycle_A: 1.004 idt_A: 0.402 D_B: 0.122 G_B: 0.376 cycle_B: 0.747 idt_B: 0.563 
End of epoch 87 / 110 	 Time Taken: 234 sec
learning rate 0.0000455 -> 0.0000436
(epoch: 88, iters: 100, time: 1.000, data: 0.180) D_A: 0.155 G_A: 0.486 cycle_A: 0.883 idt_A: 0.463 D_B: 0.130 G_B: 0.362 cycle_B: 1.003 idt_B: 0.591 
(epoch: 88, iters: 200, time: 0.573, data: 0.003) D_A: 0.166 G_A: 0.582 cycle_A: 0.758 idt_A: 0.439 D_B: 0.116 G_B: 0.469 cycle_B: 0.900 idt_B: 0.448 
(epoch: 88, iters: 300, time: 0.572, data: 0.003) D_A: 0.208 G_A: 0.304 cycle_A: 1.113 idt_A: 0.469 D_B: 0.251 G_B: 0.506 cycle_B: 0.961 idt_B: 0.721 
(epoch: 88, iters: 400, time: 0.575, data: 0.003) D_A: 0.215 G_A: 0.405 cycle_A: 0.871 idt_A: 0.362 D_B: 0.166 G_B: 0.270 cycle_B: 0.786 idt_B: 0.537 
(epoch: 88, iters: 500, time: 0.805, data: 0.003) D_A: 0.186 G_A: 0.420 cycle_A: 0.936 idt_A: 0.434 D_B: 0.191 G_B: 0.330 cycle_B: 0.987 idt_B: 0.410 
End of epoch 88 / 110 	 Time Taken: 236 sec
learning rate 0.0000436 -> 0.0000416
(epoch: 89, iters: 100, time: 0.574, data: 0.155) D_A: 0.158 G_A: 0.237 cycle_A: 0.888 idt_A: 0.412 D_B: 0.256 G_B: 0.255 cycle_B: 0.836 idt_B: 0.508 
(epoch: 89, iters: 200, time: 0.572, data: 0.003) D_A: 0.156 G_A: 0.256 cycle_A: 1.013 idt_A: 0.437 D_B: 0.111 G_B: 0.695 cycle_B: 0.890 idt_B: 0.686 
(epoch: 89, iters: 300, time: 0.576, data: 0.003) D_A: 0.117 G_A: 0.423 cycle_A: 0.907 idt_A: 0.418 D_B: 0.117 G_B: 0.297 cycle_B: 0.852 idt_B: 0.515 
(epoch: 89, iters: 400, time: 0.862, data: 0.003) D_A: 0.270 G_A: 0.394 cycle_A: 0.907 idt_A: 0.432 D_B: 0.303 G_B: 0.289 cycle_B: 0.947 idt_B: 0.499 
(epoch: 89, iters: 500, time: 0.574, data: 0.003) D_A: 0.141 G_A: 0.419 cycle_A: 0.871 idt_A: 0.296 D_B: 0.127 G_B: 0.333 cycle_B: 0.685 idt_B: 0.439 
End of epoch 89 / 110 	 Time Taken: 234 sec
learning rate 0.0000416 -> 0.0000396
(epoch: 90, iters: 100, time: 0.571, data: 0.166) D_A: 0.150 G_A: 0.411 cycle_A: 1.125 idt_A: 0.722 D_B: 0.123 G_B: 0.439 cycle_B: 1.423 idt_B: 0.487 
(epoch: 90, iters: 200, time: 0.576, data: 0.003) D_A: 0.181 G_A: 0.253 cycle_A: 0.988 idt_A: 0.336 D_B: 0.165 G_B: 0.580 cycle_B: 0.721 idt_B: 0.462 
(epoch: 90, iters: 300, time: 0.890, data: 0.003) D_A: 0.134 G_A: 0.535 cycle_A: 0.828 idt_A: 0.418 D_B: 0.098 G_B: 0.483 cycle_B: 0.799 idt_B: 0.387 
(epoch: 90, iters: 400, time: 0.573, data: 0.003) D_A: 0.108 G_A: 0.474 cycle_A: 1.060 idt_A: 0.440 D_B: 0.113 G_B: 0.400 cycle_B: 0.909 idt_B: 0.547 
(epoch: 90, iters: 500, time: 0.576, data: 0.003) D_A: 0.203 G_A: 0.369 cycle_A: 0.916 idt_A: 0.450 D_B: 0.152 G_B: 0.427 cycle_B: 0.970 idt_B: 0.511 
saving the latest model (epoch 90, total_iters 45000)
saving the model at the end of epoch 90, iters 45000
End of epoch 90 / 110 	 Time Taken: 236 sec
learning rate 0.0000396 -> 0.0000376
(epoch: 91, iters: 100, time: 0.574, data: 0.144) D_A: 0.132 G_A: 0.524 cycle_A: 1.121 idt_A: 0.400 D_B: 0.165 G_B: 0.334 cycle_B: 0.879 idt_B: 0.647 
(epoch: 91, iters: 200, time: 0.943, data: 0.003) D_A: 0.161 G_A: 0.390 cycle_A: 0.974 idt_A: 0.413 D_B: 0.195 G_B: 0.578 cycle_B: 0.916 idt_B: 0.464 
(epoch: 91, iters: 300, time: 0.575, data: 0.003) D_A: 0.170 G_A: 0.451 cycle_A: 0.826 idt_A: 0.409 D_B: 0.165 G_B: 0.362 cycle_B: 0.853 idt_B: 0.431 
(epoch: 91, iters: 400, time: 0.574, data: 0.003) D_A: 0.187 G_A: 0.540 cycle_A: 1.037 idt_A: 0.386 D_B: 0.141 G_B: 0.243 cycle_B: 0.776 idt_B: 0.561 
(epoch: 91, iters: 500, time: 0.575, data: 0.003) D_A: 0.221 G_A: 0.343 cycle_A: 0.990 idt_A: 0.391 D_B: 0.155 G_B: 0.390 cycle_B: 0.758 idt_B: 0.502 
End of epoch 91 / 110 	 Time Taken: 235 sec
learning rate 0.0000376 -> 0.0000356
(epoch: 92, iters: 100, time: 0.882, data: 0.140) D_A: 0.174 G_A: 0.513 cycle_A: 1.029 idt_A: 0.521 D_B: 0.141 G_B: 0.458 cycle_B: 1.069 idt_B: 0.461 
(epoch: 92, iters: 200, time: 0.575, data: 0.003) D_A: 0.117 G_A: 0.408 cycle_A: 1.128 idt_A: 0.310 D_B: 0.175 G_B: 0.453 cycle_B: 0.763 idt_B: 0.567 
(epoch: 92, iters: 300, time: 0.575, data: 0.003) D_A: 0.216 G_A: 0.290 cycle_A: 0.902 idt_A: 0.611 D_B: 0.269 G_B: 0.309 cycle_B: 1.074 idt_B: 0.446 
(epoch: 92, iters: 400, time: 0.575, data: 0.003) D_A: 0.321 G_A: 0.369 cycle_A: 0.881 idt_A: 0.469 D_B: 0.149 G_B: 0.466 cycle_B: 0.859 idt_B: 0.453 
(epoch: 92, iters: 500, time: 0.800, data: 0.003) D_A: 0.174 G_A: 0.318 cycle_A: 0.807 idt_A: 0.346 D_B: 0.165 G_B: 0.475 cycle_B: 0.836 idt_B: 0.400 
End of epoch 92 / 110 	 Time Taken: 236 sec
learning rate 0.0000356 -> 0.0000337
(epoch: 93, iters: 100, time: 0.575, data: 0.145) D_A: 0.183 G_A: 0.417 cycle_A: 0.766 idt_A: 0.493 D_B: 0.103 G_B: 0.564 cycle_B: 0.911 idt_B: 0.493 
(epoch: 93, iters: 200, time: 0.574, data: 0.003) D_A: 0.134 G_A: 0.495 cycle_A: 1.129 idt_A: 0.409 D_B: 0.132 G_B: 0.460 cycle_B: 0.929 idt_B: 0.645 
(epoch: 93, iters: 300, time: 0.574, data: 0.003) D_A: 0.103 G_A: 0.528 cycle_A: 0.953 idt_A: 0.359 D_B: 0.162 G_B: 0.364 cycle_B: 0.871 idt_B: 0.516 
(epoch: 93, iters: 400, time: 0.933, data: 0.003) D_A: 0.099 G_A: 0.555 cycle_A: 1.142 idt_A: 0.329 D_B: 0.157 G_B: 0.388 cycle_B: 0.688 idt_B: 0.515 
(epoch: 93, iters: 500, time: 0.574, data: 0.003) D_A: 0.165 G_A: 0.407 cycle_A: 1.023 idt_A: 0.392 D_B: 0.169 G_B: 0.354 cycle_B: 0.903 idt_B: 0.522 
End of epoch 93 / 110 	 Time Taken: 235 sec
learning rate 0.0000337 -> 0.0000317
(epoch: 94, iters: 100, time: 0.573, data: 0.139) D_A: 0.201 G_A: 0.486 cycle_A: 0.898 idt_A: 0.491 D_B: 0.141 G_B: 0.345 cycle_B: 0.992 idt_B: 0.453 
(epoch: 94, iters: 200, time: 0.574, data: 0.004) D_A: 0.097 G_A: 0.395 cycle_A: 0.971 idt_A: 0.460 D_B: 0.170 G_B: 0.489 cycle_B: 0.861 idt_B: 0.520 
(epoch: 94, iters: 300, time: 0.965, data: 0.003) D_A: 0.123 G_A: 0.488 cycle_A: 1.284 idt_A: 0.413 D_B: 0.135 G_B: 0.414 cycle_B: 0.896 idt_B: 0.818 
(epoch: 94, iters: 400, time: 0.574, data: 0.003) D_A: 0.199 G_A: 0.322 cycle_A: 1.123 idt_A: 0.481 D_B: 0.145 G_B: 0.554 cycle_B: 1.262 idt_B: 0.529 
(epoch: 94, iters: 500, time: 0.575, data: 0.003) D_A: 0.159 G_A: 0.547 cycle_A: 1.074 idt_A: 0.448 D_B: 0.150 G_B: 0.346 cycle_B: 0.876 idt_B: 0.529 
End of epoch 94 / 110 	 Time Taken: 235 sec
learning rate 0.0000317 -> 0.0000297
(epoch: 95, iters: 100, time: 0.574, data: 0.134) D_A: 0.163 G_A: 0.336 cycle_A: 0.855 idt_A: 0.418 D_B: 0.178 G_B: 0.515 cycle_B: 0.792 idt_B: 0.451 
(epoch: 95, iters: 200, time: 0.870, data: 0.003) D_A: 0.173 G_A: 0.416 cycle_A: 1.137 idt_A: 0.472 D_B: 0.185 G_B: 0.620 cycle_B: 0.892 idt_B: 0.540 
(epoch: 95, iters: 300, time: 0.574, data: 0.002) D_A: 0.167 G_A: 0.365 cycle_A: 0.936 idt_A: 0.322 D_B: 0.154 G_B: 0.482 cycle_B: 0.772 idt_B: 0.456 
(epoch: 95, iters: 400, time: 0.575, data: 0.003) D_A: 0.153 G_A: 0.351 cycle_A: 0.931 idt_A: 0.493 D_B: 0.164 G_B: 0.427 cycle_B: 1.074 idt_B: 0.603 
(epoch: 95, iters: 500, time: 0.576, data: 0.003) D_A: 0.098 G_A: 0.512 cycle_A: 0.824 idt_A: 0.354 D_B: 0.179 G_B: 0.477 cycle_B: 0.637 idt_B: 0.442 
saving the model at the end of epoch 95, iters 47500
End of epoch 95 / 110 	 Time Taken: 236 sec
learning rate 0.0000297 -> 0.0000277
(epoch: 96, iters: 100, time: 0.863, data: 0.153) D_A: 0.147 G_A: 0.653 cycle_A: 0.857 idt_A: 0.315 D_B: 0.087 G_B: 0.471 cycle_B: 0.712 idt_B: 0.481 
(epoch: 96, iters: 200, time: 0.574, data: 0.002) D_A: 0.121 G_A: 0.414 cycle_A: 0.931 idt_A: 0.338 D_B: 0.144 G_B: 0.614 cycle_B: 0.820 idt_B: 0.415 
(epoch: 96, iters: 300, time: 0.574, data: 0.003) D_A: 0.207 G_A: 0.424 cycle_A: 1.068 idt_A: 0.437 D_B: 0.094 G_B: 0.603 cycle_B: 0.951 idt_B: 0.496 
(epoch: 96, iters: 400, time: 0.575, data: 0.004) D_A: 0.158 G_A: 0.458 cycle_A: 0.889 idt_A: 0.418 D_B: 0.179 G_B: 0.241 cycle_B: 1.041 idt_B: 0.422 
(epoch: 96, iters: 500, time: 0.801, data: 0.003) D_A: 0.133 G_A: 0.347 cycle_A: 0.967 idt_A: 0.455 D_B: 0.164 G_B: 0.381 cycle_B: 0.963 idt_B: 0.493 
End of epoch 96 / 110 	 Time Taken: 235 sec
learning rate 0.0000277 -> 0.0000257
(epoch: 97, iters: 100, time: 0.574, data: 0.143) D_A: 0.174 G_A: 0.432 cycle_A: 0.794 idt_A: 0.447 D_B: 0.143 G_B: 0.290 cycle_B: 0.977 idt_B: 0.439 
(epoch: 97, iters: 200, time: 0.573, data: 0.003) D_A: 0.108 G_A: 0.533 cycle_A: 0.847 idt_A: 0.324 D_B: 0.109 G_B: 0.494 cycle_B: 0.701 idt_B: 0.451 
(epoch: 97, iters: 300, time: 0.574, data: 0.003) D_A: 0.164 G_A: 0.458 cycle_A: 0.943 idt_A: 0.472 D_B: 0.209 G_B: 0.356 cycle_B: 0.952 idt_B: 0.434 
(epoch: 97, iters: 400, time: 0.881, data: 0.003) D_A: 0.153 G_A: 0.323 cycle_A: 0.985 idt_A: 0.327 D_B: 0.213 G_B: 0.406 cycle_B: 0.751 idt_B: 0.515 
(epoch: 97, iters: 500, time: 0.574, data: 0.003) D_A: 0.185 G_A: 0.337 cycle_A: 0.772 idt_A: 0.401 D_B: 0.215 G_B: 0.354 cycle_B: 0.852 idt_B: 0.349 
End of epoch 97 / 110 	 Time Taken: 235 sec
learning rate 0.0000257 -> 0.0000238
(epoch: 98, iters: 100, time: 0.576, data: 0.161) D_A: 0.189 G_A: 0.447 cycle_A: 1.072 idt_A: 0.502 D_B: 0.200 G_B: 0.418 cycle_B: 0.982 idt_B: 0.526 
(epoch: 98, iters: 200, time: 0.575, data: 0.003) D_A: 0.217 G_A: 0.425 cycle_A: 0.805 idt_A: 0.352 D_B: 0.208 G_B: 0.372 cycle_B: 0.687 idt_B: 0.432 
(epoch: 98, iters: 300, time: 0.948, data: 0.003) D_A: 0.182 G_A: 0.364 cycle_A: 1.098 idt_A: 0.395 D_B: 0.184 G_B: 0.339 cycle_B: 0.819 idt_B: 0.576 
(epoch: 98, iters: 400, time: 0.575, data: 0.003) D_A: 0.162 G_A: 0.483 cycle_A: 0.812 idt_A: 0.481 D_B: 0.159 G_B: 0.718 cycle_B: 0.874 idt_B: 0.395 
(epoch: 98, iters: 500, time: 0.576, data: 0.003) D_A: 0.214 G_A: 0.447 cycle_A: 0.724 idt_A: 0.568 D_B: 0.173 G_B: 0.533 cycle_B: 1.019 idt_B: 0.405 
End of epoch 98 / 110 	 Time Taken: 235 sec
learning rate 0.0000238 -> 0.0000218
(epoch: 99, iters: 100, time: 0.576, data: 0.140) D_A: 0.137 G_A: 0.455 cycle_A: 0.708 idt_A: 0.345 D_B: 0.131 G_B: 0.457 cycle_B: 0.701 idt_B: 0.388 
(epoch: 99, iters: 200, time: 0.899, data: 0.003) D_A: 0.158 G_A: 0.296 cycle_A: 1.040 idt_A: 0.306 D_B: 0.237 G_B: 0.443 cycle_B: 0.624 idt_B: 0.543 
(epoch: 99, iters: 300, time: 0.574, data: 0.003) D_A: 0.139 G_A: 0.369 cycle_A: 0.996 idt_A: 0.316 D_B: 0.134 G_B: 0.527 cycle_B: 0.714 idt_B: 0.473 
(epoch: 99, iters: 400, time: 0.574, data: 0.003) D_A: 0.164 G_A: 0.571 cycle_A: 0.981 idt_A: 0.405 D_B: 0.209 G_B: 0.442 cycle_B: 0.874 idt_B: 0.475 
(epoch: 99, iters: 500, time: 0.575, data: 0.003) D_A: 0.195 G_A: 0.368 cycle_A: 0.918 idt_A: 0.355 D_B: 0.129 G_B: 0.378 cycle_B: 0.815 idt_B: 0.493 
End of epoch 99 / 110 	 Time Taken: 235 sec
learning rate 0.0000218 -> 0.0000198
(epoch: 100, iters: 100, time: 0.871, data: 0.204) D_A: 0.279 G_A: 0.387 cycle_A: 1.027 idt_A: 0.373 D_B: 0.180 G_B: 0.321 cycle_B: 0.723 idt_B: 0.504 
(epoch: 100, iters: 200, time: 0.573, data: 0.003) D_A: 0.192 G_A: 0.438 cycle_A: 0.994 idt_A: 0.343 D_B: 0.209 G_B: 0.301 cycle_B: 0.726 idt_B: 0.502 
(epoch: 100, iters: 300, time: 0.574, data: 0.003) D_A: 0.102 G_A: 0.651 cycle_A: 0.919 idt_A: 0.368 D_B: 0.166 G_B: 0.376 cycle_B: 0.663 idt_B: 0.494 
(epoch: 100, iters: 400, time: 0.574, data: 0.003) D_A: 0.206 G_A: 0.387 cycle_A: 0.941 idt_A: 0.425 D_B: 0.169 G_B: 0.304 cycle_B: 0.869 idt_B: 0.489 
(epoch: 100, iters: 500, time: 0.866, data: 0.003) D_A: 0.129 G_A: 0.349 cycle_A: 1.001 idt_A: 0.425 D_B: 0.147 G_B: 0.438 cycle_B: 0.822 idt_B: 0.498 
saving the latest model (epoch 100, total_iters 50000)
saving the model at the end of epoch 100, iters 50000
End of epoch 100 / 110 	 Time Taken: 237 sec
learning rate 0.0000198 -> 0.0000178
(epoch: 101, iters: 100, time: 0.574, data: 0.142) D_A: 0.096 G_A: 0.378 cycle_A: 0.927 idt_A: 0.329 D_B: 0.130 G_B: 0.611 cycle_B: 0.575 idt_B: 0.550 
(epoch: 101, iters: 200, time: 0.575, data: 0.003) D_A: 0.156 G_A: 0.405 cycle_A: 0.879 idt_A: 0.384 D_B: 0.196 G_B: 0.428 cycle_B: 0.811 idt_B: 0.435 
(epoch: 101, iters: 300, time: 0.574, data: 0.003) D_A: 0.122 G_A: 0.361 cycle_A: 0.833 idt_A: 0.310 D_B: 0.206 G_B: 0.539 cycle_B: 0.792 idt_B: 0.445 
(epoch: 101, iters: 400, time: 0.878, data: 0.003) D_A: 0.199 G_A: 0.349 cycle_A: 0.997 idt_A: 0.495 D_B: 0.157 G_B: 0.298 cycle_B: 1.031 idt_B: 0.567 
(epoch: 101, iters: 500, time: 0.576, data: 0.003) D_A: 0.150 G_A: 0.490 cycle_A: 0.852 idt_A: 0.406 D_B: 0.131 G_B: 0.406 cycle_B: 0.839 idt_B: 0.460 
End of epoch 101 / 110 	 Time Taken: 235 sec
learning rate 0.0000178 -> 0.0000158
(epoch: 102, iters: 100, time: 0.576, data: 0.146) D_A: 0.157 G_A: 0.343 cycle_A: 0.917 idt_A: 0.425 D_B: 0.185 G_B: 0.332 cycle_B: 0.847 idt_B: 0.530 
(epoch: 102, iters: 200, time: 0.576, data: 0.003) D_A: 0.162 G_A: 0.523 cycle_A: 1.040 idt_A: 0.414 D_B: 0.123 G_B: 0.372 cycle_B: 0.935 idt_B: 0.591 
(epoch: 102, iters: 300, time: 0.885, data: 0.002) D_A: 0.234 G_A: 0.384 cycle_A: 1.015 idt_A: 0.383 D_B: 0.137 G_B: 0.290 cycle_B: 0.805 idt_B: 0.791 
(epoch: 102, iters: 400, time: 0.575, data: 0.003) D_A: 0.149 G_A: 0.377 cycle_A: 0.906 idt_A: 0.335 D_B: 0.149 G_B: 0.336 cycle_B: 0.666 idt_B: 0.385 
(epoch: 102, iters: 500, time: 0.575, data: 0.003) D_A: 0.234 G_A: 0.393 cycle_A: 0.909 idt_A: 0.427 D_B: 0.233 G_B: 0.425 cycle_B: 0.820 idt_B: 0.505 
End of epoch 102 / 110 	 Time Taken: 235 sec
learning rate 0.0000158 -> 0.0000139
(epoch: 103, iters: 100, time: 0.576, data: 0.143) D_A: 0.178 G_A: 0.427 cycle_A: 0.972 idt_A: 0.462 D_B: 0.163 G_B: 0.500 cycle_B: 0.921 idt_B: 0.543 
(epoch: 103, iters: 200, time: 0.895, data: 0.003) D_A: 0.157 G_A: 0.472 cycle_A: 0.873 idt_A: 0.352 D_B: 0.148 G_B: 0.439 cycle_B: 0.791 idt_B: 0.472 
(epoch: 103, iters: 300, time: 0.575, data: 0.004) D_A: 0.156 G_A: 0.429 cycle_A: 0.873 idt_A: 0.398 D_B: 0.178 G_B: 0.401 cycle_B: 0.750 idt_B: 0.533 
(epoch: 103, iters: 400, time: 0.575, data: 0.003) D_A: 0.096 G_A: 0.449 cycle_A: 0.790 idt_A: 0.374 D_B: 0.177 G_B: 0.584 cycle_B: 0.812 idt_B: 0.492 
(epoch: 103, iters: 500, time: 0.575, data: 0.003) D_A: 0.204 G_A: 0.493 cycle_A: 1.049 idt_A: 0.364 D_B: 0.157 G_B: 0.313 cycle_B: 0.709 idt_B: 0.552 
End of epoch 103 / 110 	 Time Taken: 235 sec
learning rate 0.0000139 -> 0.0000119
(epoch: 104, iters: 100, time: 0.947, data: 0.153) D_A: 0.185 G_A: 0.474 cycle_A: 0.758 idt_A: 0.394 D_B: 0.113 G_B: 0.731 cycle_B: 0.789 idt_B: 0.532 
(epoch: 104, iters: 200, time: 0.574, data: 0.002) D_A: 0.180 G_A: 0.586 cycle_A: 0.832 idt_A: 0.444 D_B: 0.220 G_B: 0.331 cycle_B: 0.942 idt_B: 0.450 
(epoch: 104, iters: 300, time: 0.574, data: 0.003) D_A: 0.246 G_A: 0.370 cycle_A: 0.815 idt_A: 0.372 D_B: 0.268 G_B: 0.323 cycle_B: 0.761 idt_B: 0.443 
(epoch: 104, iters: 400, time: 0.575, data: 0.003) D_A: 0.117 G_A: 0.453 cycle_A: 1.009 idt_A: 0.308 D_B: 0.190 G_B: 0.483 cycle_B: 0.751 idt_B: 0.477 
(epoch: 104, iters: 500, time: 0.836, data: 0.003) D_A: 0.107 G_A: 0.447 cycle_A: 0.815 idt_A: 0.330 D_B: 0.186 G_B: 0.358 cycle_B: 0.689 idt_B: 0.407 
End of epoch 104 / 110 	 Time Taken: 236 sec
learning rate 0.0000119 -> 0.0000099
(epoch: 105, iters: 100, time: 0.574, data: 0.137) D_A: 0.119 G_A: 0.435 cycle_A: 0.704 idt_A: 0.271 D_B: 0.232 G_B: 0.485 cycle_B: 0.678 idt_B: 0.431 
(epoch: 105, iters: 200, time: 0.574, data: 0.003) D_A: 0.160 G_A: 0.387 cycle_A: 0.900 idt_A: 0.378 D_B: 0.163 G_B: 0.437 cycle_B: 0.751 idt_B: 0.484 
(epoch: 105, iters: 300, time: 0.575, data: 0.003) D_A: 0.156 G_A: 0.506 cycle_A: 0.698 idt_A: 0.409 D_B: 0.178 G_B: 0.273 cycle_B: 0.845 idt_B: 0.373 
(epoch: 105, iters: 400, time: 0.888, data: 0.003) D_A: 0.129 G_A: 0.544 cycle_A: 0.741 idt_A: 0.425 D_B: 0.120 G_B: 0.339 cycle_B: 0.668 idt_B: 0.374 
(epoch: 105, iters: 500, time: 0.574, data: 0.003) D_A: 0.177 G_A: 0.342 cycle_A: 0.945 idt_A: 0.338 D_B: 0.213 G_B: 0.380 cycle_B: 0.717 idt_B: 0.456 
saving the model at the end of epoch 105, iters 52500
End of epoch 105 / 110 	 Time Taken: 236 sec
learning rate 0.0000099 -> 0.0000079
(epoch: 106, iters: 100, time: 0.574, data: 0.168) D_A: 0.125 G_A: 0.422 cycle_A: 0.873 idt_A: 0.357 D_B: 0.143 G_B: 0.422 cycle_B: 0.771 idt_B: 0.422 
(epoch: 106, iters: 200, time: 0.575, data: 0.003) D_A: 0.119 G_A: 0.478 cycle_A: 1.036 idt_A: 0.336 D_B: 0.181 G_B: 0.607 cycle_B: 0.654 idt_B: 0.485 
(epoch: 106, iters: 300, time: 0.895, data: 0.003) D_A: 0.163 G_A: 0.331 cycle_A: 0.704 idt_A: 0.383 D_B: 0.200 G_B: 0.409 cycle_B: 0.754 idt_B: 0.365 
(epoch: 106, iters: 400, time: 0.575, data: 0.003) D_A: 0.152 G_A: 0.588 cycle_A: 0.868 idt_A: 0.439 D_B: 0.112 G_B: 0.445 cycle_B: 0.890 idt_B: 0.388 
(epoch: 106, iters: 500, time: 0.574, data: 0.004) D_A: 0.144 G_A: 0.479 cycle_A: 0.975 idt_A: 0.439 D_B: 0.144 G_B: 0.482 cycle_B: 0.871 idt_B: 0.658 
End of epoch 106 / 110 	 Time Taken: 235 sec
learning rate 0.0000079 -> 0.0000059
(epoch: 107, iters: 100, time: 0.575, data: 0.145) D_A: 0.122 G_A: 0.498 cycle_A: 0.879 idt_A: 0.312 D_B: 0.174 G_B: 0.563 cycle_B: 0.654 idt_B: 0.483 
(epoch: 107, iters: 200, time: 0.952, data: 0.003) D_A: 0.222 G_A: 0.431 cycle_A: 0.989 idt_A: 0.597 D_B: 0.181 G_B: 0.550 cycle_B: 1.160 idt_B: 0.467 
(epoch: 107, iters: 300, time: 0.576, data: 0.003) D_A: 0.242 G_A: 0.270 cycle_A: 1.184 idt_A: 0.443 D_B: 0.169 G_B: 0.499 cycle_B: 0.873 idt_B: 0.583 
(epoch: 107, iters: 400, time: 0.576, data: 0.003) D_A: 0.098 G_A: 0.532 cycle_A: 0.738 idt_A: 0.299 D_B: 0.083 G_B: 0.589 cycle_B: 0.630 idt_B: 0.343 
(epoch: 107, iters: 500, time: 0.576, data: 0.003) D_A: 0.140 G_A: 0.538 cycle_A: 1.040 idt_A: 0.288 D_B: 0.139 G_B: 0.486 cycle_B: 0.611 idt_B: 0.558 
End of epoch 107 / 110 	 Time Taken: 235 sec
learning rate 0.0000059 -> 0.0000040
(epoch: 108, iters: 100, time: 0.912, data: 0.149) D_A: 0.105 G_A: 0.567 cycle_A: 0.819 idt_A: 0.268 D_B: 0.154 G_B: 0.458 cycle_B: 0.579 idt_B: 0.371 
(epoch: 108, iters: 200, time: 0.574, data: 0.003) D_A: 0.197 G_A: 0.538 cycle_A: 0.830 idt_A: 0.522 D_B: 0.099 G_B: 0.457 cycle_B: 1.112 idt_B: 0.348 
(epoch: 108, iters: 300, time: 0.575, data: 0.003) D_A: 0.187 G_A: 0.608 cycle_A: 0.808 idt_A: 0.355 D_B: 0.128 G_B: 0.419 cycle_B: 0.715 idt_B: 0.430 
(epoch: 108, iters: 400, time: 0.575, data: 0.003) D_A: 0.116 G_A: 0.525 cycle_A: 1.023 idt_A: 0.309 D_B: 0.170 G_B: 0.415 cycle_B: 0.633 idt_B: 0.549 
(epoch: 108, iters: 500, time: 0.840, data: 0.003) D_A: 0.205 G_A: 0.412 cycle_A: 0.935 idt_A: 0.352 D_B: 0.210 G_B: 0.367 cycle_B: 0.839 idt_B: 0.588 
End of epoch 108 / 110 	 Time Taken: 236 sec
learning rate 0.0000040 -> 0.0000020
(epoch: 109, iters: 100, time: 0.576, data: 0.139) D_A: 0.201 G_A: 0.426 cycle_A: 0.999 idt_A: 0.400 D_B: 0.160 G_B: 0.449 cycle_B: 0.838 idt_B: 0.443 
(epoch: 109, iters: 200, time: 0.573, data: 0.003) D_A: 0.194 G_A: 0.403 cycle_A: 0.832 idt_A: 0.287 D_B: 0.153 G_B: 0.389 cycle_B: 0.681 idt_B: 0.403 
(epoch: 109, iters: 300, time: 0.575, data: 0.003) D_A: 0.146 G_A: 0.486 cycle_A: 0.819 idt_A: 0.266 D_B: 0.204 G_B: 0.535 cycle_B: 0.607 idt_B: 0.395 
(epoch: 109, iters: 400, time: 0.901, data: 0.004) D_A: 0.136 G_A: 0.429 cycle_A: 0.981 idt_A: 0.291 D_B: 0.153 G_B: 0.363 cycle_B: 0.693 idt_B: 0.502 
(epoch: 109, iters: 500, time: 0.575, data: 0.003) D_A: 0.215 G_A: 0.442 cycle_A: 0.761 idt_A: 0.421 D_B: 0.122 G_B: 0.366 cycle_B: 0.886 idt_B: 0.511 
End of epoch 109 / 110 	 Time Taken: 235 sec
learning rate 0.0000020 -> 0.0000000
(epoch: 110, iters: 100, time: 0.576, data: 0.140) D_A: 0.223 G_A: 0.351 cycle_A: 0.682 idt_A: 0.390 D_B: 0.168 G_B: 0.443 cycle_B: 0.832 idt_B: 0.338 
(epoch: 110, iters: 200, time: 0.576, data: 0.003) D_A: 0.138 G_A: 0.501 cycle_A: 0.749 idt_A: 0.398 D_B: 0.157 G_B: 0.402 cycle_B: 0.837 idt_B: 0.440 
(epoch: 110, iters: 300, time: 0.962, data: 0.003) D_A: 0.186 G_A: 0.511 cycle_A: 1.036 idt_A: 0.429 D_B: 0.156 G_B: 0.500 cycle_B: 0.948 idt_B: 0.531 
(epoch: 110, iters: 400, time: 0.576, data: 0.003) D_A: 0.098 G_A: 0.455 cycle_A: 1.032 idt_A: 0.297 D_B: 0.161 G_B: 0.539 cycle_B: 0.676 idt_B: 0.473 
(epoch: 110, iters: 500, time: 0.578, data: 0.003) D_A: 0.126 G_A: 0.364 cycle_A: 0.823 idt_A: 0.532 D_B: 0.212 G_B: 0.571 cycle_B: 1.056 idt_B: 0.481 
saving the latest model (epoch 110, total_iters 55000)
saving the model at the end of epoch 110, iters 55000
End of epoch 110 / 110 	 Time Taken: 237 sec
wandb: 🚀 View run monet2photo at: https://wandb.ai/gongliangtju-clayco/CycleGAN-and-pix2pix/runs/qyxr0rh1
wandb: Find logs at: wandb/run-20241119_211915-qyxr0rh1/logs

Training Log Legend

# (epoch: X, iters: Y, time: Z, data: W)

# - **epoch**: Current epoch number
# - **iters**: Current iteration within the epoch
# - **time**: Processing time per iteration (seconds)
# - **data**: Data loading time per iteration (seconds)


# D_A / D_B: Discriminator loss for Domain A / Domain B
# G_A / G_B: Generator loss for translating from Domain A to B (G_A) or B to A (G_B)

# cycle_A / cycle_B: Cycle-consistency loss ensuring image reconstruction (A -> B -> A and B -> A -> B)
# idt_A / idt_B: Identity loss for preserving features in images already in the target domain (A or B)

# Lower values generally indicate better performance and stability.

CycleGAN WandB Metrics Explained

real_A / real_B: Original images from Domain A (e.g., monet) and Domain B (e.g., real).

fake_B / fake_A: Generated images by translating real_A to Domain B (e.g., monet to real) and real_B to Domain A (e.g., real to monet).

---

rec_A / rec_B: Reconstructed images in each domain after a cycle (A → B → A, B → A → B). Should resemble real_A and real_B closely.

idt_A / idt_B: Identity-preserved images when no translation is applied. idt_A should look like real_A; idt_B should look like real_B.

Model testing

Runs the model in test mode, using images from the test set.

Testing Command:

# Model Testing:
# After training CycleGAN, we can test it to generate and view translated images between the two domains (e.g., monet to real and vice versa).

# Command to Test the Model:
# This command runs the model on the test set, generating images that will be saved in the results directory.
!python test.py \
    --dataroot ./datasets/monet2photo \
    --name monet2photo \
    --model cycle_gan \
    --phase test

# Explanation of the Command:
# - `--dataroot`: Specifies the path to the dataset.
# - `--name`: Sets the experiment name (should match the name used during training).
# - `--model`: Specifies the model type as 'cycle_gan'.
# - `--phase test`: Runs the model in test mode, using images from the test set.



# Results:
# Generated images will be saved in a new directory under `./results/monet2photo/test_latest`.
# This will include translated images for both directions (e.g., monet to real and real to monet).
# You can view these images to assess the quality of the model's translations.

----------------- Options ---------------
             aspect_ratio: 1.0                           
               batch_size: 1                             
          checkpoints_dir: ./checkpoints                 
                crop_size: 256                           
                 dataroot: ./datasets/monet2photo        	[default: None]
             dataset_mode: unaligned                     
                direction: AtoB                          
          display_winsize: 256                           
                    epoch: latest                        
                     eval: False                         
                  gpu_ids: 0                             
                init_gain: 0.02                          
                init_type: normal                        
                 input_nc: 3                             
                  isTrain: False                         	[default: None]
                load_iter: 0                             	[default: 0]
                load_size: 256                           
         max_dataset_size: inf                           
                    model: cycle_gan                     	[default: test]
               n_layers_D: 3                             
                     name: monet2photo                   	[default: experiment_name]
                      ndf: 64                            
                     netD: basic                         
                     netG: resnet_9blocks                
                      ngf: 64                            
               no_dropout: True                          
                  no_flip: False                         
                     norm: instance                      
                 num_test: 50                            
              num_threads: 4                             
                output_nc: 3                             
                    phase: test                          
               preprocess: resize_and_crop               
              results_dir: ./results/                    
           serial_batches: False                         
                   suffix:                               
                use_wandb: False                         
                  verbose: False                         
       wandb_project_name: CycleGAN-and-pix2pix          
----------------- End -------------------
dataset [UnalignedDataset] was created
initialize network with normal
initialize network with normal
model [CycleGANModel] was created
loading the model from ./checkpoints/monet2photo/latest_net_G_A.pth
/content/pytorch-CycleGAN-and-pix2pix/models/base_model.py:192: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = torch.load(load_path, map_location=str(self.device))
loading the model from ./checkpoints/monet2photo/latest_net_G_B.pth
---------- Networks initialized -------------
[Network G_A] Total number of parameters : 11.378 M
[Network G_B] Total number of parameters : 11.378 M
-----------------------------------------------
creating web directory ./results/monet2photo/test_latest
processing (0000)-th image... ['./datasets/monet2photo/testA/00010.jpg']
processing (0005)-th image... ['./datasets/monet2photo/testA/00060.jpg']
processing (0010)-th image... ['./datasets/monet2photo/testA/00110.jpg']
processing (0015)-th image... ['./datasets/monet2photo/testA/00160.jpg']
processing (0020)-th image... ['./datasets/monet2photo/testA/00210.jpg']
processing (0025)-th image... ['./datasets/monet2photo/testA/00270.jpg']
processing (0030)-th image... ['./datasets/monet2photo/testA/00320.jpg']
processing (0035)-th image... ['./datasets/monet2photo/testA/00390.jpg']
processing (0040)-th image... ['./datasets/monet2photo/testA/00440.jpg']
processing (0045)-th image... ['./datasets/monet2photo/testA/00490.jpg']

• 00010_real_A means real monet painting in domain a, and 00010_fake_B means fake normal image in domain B which corresponds to 00010_real_A.

• 00010_real_B means real normal picture in domain b, and 00010_fake_A means fake monet painting in domain A which corresponds to 00010_real_B.

In other words -

• 00010_real_A: Real Monet painting (domain A).
• 00010_fake_B: Fake normal image (domain B) generated from 00010_real_A.
• 00010_real_B: Real normal picture (domain B).
• 00010_fake_A: Fake Monet painting (domain A) generated from 00010_real_B.

Visualize Real vs. Fake Image

import matplotlib.pyplot as plt
from PIL import Image

# Define paths to the real and fake images
real_image_path = "./results/monet2photo/test_latest/images/00010_real_A.png"
fake_image_path = "./results/monet2photo/test_latest/images/00010_fake_B.png"

# Load the images
real_image = Image.open(real_image_path)
fake_image = Image.open(fake_image_path)

# Plot the images side by side for comparison
plt.figure(figsize=(10, 5))

# Display real image
plt.subplot(1, 2, 1)
plt.imshow(real_image)
plt.title("Real Monet Painting")
plt.axis("off")

# Display fake (generated) image
plt.subplot(1, 2, 2)
plt.imshow(fake_image)
plt.title("Fake normal picture (Generated)")
plt.axis("off")

plt.tight_layout()
plt.show()

# This cell visualizes the real (Real Monet Painting) vs. fake (Fake Monet Painting (Generated)) image, allowing a quick comparison to assess the quality of the generated translation.

import matplotlib.pyplot as plt
from PIL import Image

# Define paths to the real and fake images
real_image_path = "./results/monet2photo/test_latest/images/00010_real_B.png"
fake_image_path = "./results/monet2photo/test_latest/images/00010_fake_A.png"

# Load the images
real_image = Image.open(real_image_path)
fake_image = Image.open(fake_image_path)

# Plot the images side by side for comparison
plt.figure(figsize=(10, 5))

# Add a big title to the entire figure
plt.suptitle("From Real Normal Picture 00010 to Monet Style Painting", fontsize=16, fontweight='bold')

# Display real image
plt.subplot(1, 2, 1)
plt.imshow(real_image)
plt.title("Real normal picture")
plt.axis("off")

# Display fake (generated) image
plt.subplot(1, 2, 2)
plt.imshow(fake_image)
plt.title("Fake monet painting (Generated)")
plt.axis("off")

plt.tight_layout()
plt.show()

# This cell visualizes the real (Real Monet Painting) vs. fake (Fake Monet Painting (Generated)) image, allowing a quick comparison to assess the quality of the generated translation.

import matplotlib.pyplot as plt
from PIL import Image

# Define paths to the real and fake images
real_image_path = "./results/monet2photo/test_latest/images/00020_real_B.png"
fake_image_path = "./results/monet2photo/test_latest/images/00020_fake_A.png"

# Load the images
real_image = Image.open(real_image_path)
fake_image = Image.open(fake_image_path)

# Plot the images side by side for comparison
plt.figure(figsize=(10, 5))

# Add a big title to the entire figure
plt.suptitle("From Real Normal Picture 00020 to Monet Style Painting", fontsize=16, fontweight='bold')

# Display real image
plt.subplot(1, 2, 1)
plt.imshow(real_image)
plt.title("Real normal picture")
plt.axis("off")

# Display fake (generated) image
plt.subplot(1, 2, 2)
plt.imshow(fake_image)
plt.title("Fake monet painting (Generated)")
plt.axis("off")

plt.tight_layout()
plt.show()

# This cell visualizes the real (Real normal picture) vs. fake (Fake monet painting (Generated)) image, allowing a quick comparison to assess the quality of the generated translation.

import matplotlib.pyplot as plt
from PIL import Image

# Define paths to the real and fake images
real_image_path = "./results/monet2photo/test_latest/images/00030_real_B.png"
fake_image_path = "./results/monet2photo/test_latest/images/00030_fake_A.png"

# Load the images
real_image = Image.open(real_image_path)
fake_image = Image.open(fake_image_path)

# Plot the images side by side for comparison
plt.figure(figsize=(10, 5))

# Add a big title to the entire figure
plt.suptitle("From Real Normal Picture 00030 to Monet Style Painting", fontsize=16, fontweight='bold')

# Display real image
plt.subplot(1, 2, 1)
plt.imshow(real_image)
plt.title("Real normal picture")
plt.axis("off")

# Display fake (generated) image
plt.subplot(1, 2, 2)
plt.imshow(fake_image)
plt.title("Fake monet painting (Generated)")
plt.axis("off")

plt.tight_layout()
plt.show()

# This cell visualizes the real (Real normal picture) vs. fake (Fake monet painting (Generated)) image, allowing a quick comparison to assess the quality of the generated translation.

Why CycleGAN Instead of Pix2Pix?

Choice of CycleGAN: CycleGAN was chosen over Pix2Pix because the monet2photo dataset consists of unpaired images. CycleGAN is specifically designed for unpaired image-to-image translation, meaning it can learn mappings between two domains (e.g., Monet paintings and normal photos) without requiring direct pixel-wise correspondences between images. In contrast, Pix2Pix requires paired data, where each image in one domain has a corresponding image in the other domain, which is not available in this dataset.

How CycleGAN Works with Unpaired Data:

CycleGAN uses a cycle-consistency loss to ensure the mapping is coherent.

For example:

• A real photo (domain B) is transformed into a Monet-style painting (domain A).

• The generated Monet painting is transformed back into the original photo (reconstructed B). This cycle consistency enforces meaningful translations, even without paired examples.

Insights on Model Performance based on the losses above

Strengths:

Stylistic Features Captured: The CycleGAN model effectively captured Monet's distinctive artistic style, such as:

• Color Palette: Vibrant, pastel-like colors characteristic of Monet's paintings.

• Brush Stroke Patterns: The generated images demonstrate abstract and soft textures that mimic Monet’s impressionistic brush strokes.

Limitations:

• Details in Generated Images: While the model successfully captures the artistic style, some fine details in the generated Monet-style paintings are oversimplified or appear overly smoothed, possibly due to the adversarial training process.

• Variability: Some generated results are highly stylized, while others are closer to the original photo, reflecting variability in the model's ability to generalize Monet's style across diverse images.